{"data":[{"id":"10.5281/zenodo.20099556","type":"dois","attributes":{"doi":"10.5281/zenodo.20099556","identifiers":[],"creators":[{"name":"Abinash Bharadwaj, A.Bharadwaj","nameType":"Personal","givenName":"A.Bharadwaj","familyName":"Abinash Bharadwaj","nameIdentifiers":[{"nameIdentifier":"0009-0005-2851-3101","nameIdentifierScheme":"ORCID"}],"affiliation":[]}],"titles":[{"title":"COGNITIVE DNA BY ABINASH BHARADWAJ THEORY OF C-DNA BY A.BHARADWAJ"},{"lang":"eng","title":"C-DNA: Cognitive DNA Theory","titleType":"AlternativeTitle"}],"publisher":"Zenodo","container":{},"publicationYear":2025,"subjects":[{"subject":"Cognition","subjectScheme":"MeSH"},{"subject":"Cognitive neuroscience","subjectScheme":"EuroSciVoc"},{"subject":"Cognitive psychology","subjectScheme":"EuroSciVoc"},{"subject":"Cognitive Psychology","subjectScheme":"MeSH"},{"subject":"Cognition/classification","subjectScheme":"MeSH"},{"subject":"Cognitive Neuroscience","subjectScheme":"MeSH"}],"contributors":[],"dates":[{"date":"2025-05-09","dateType":"Issued"},{"date":"2025-01-01","dateType":"Issued"}],"language":null,"types":{"ris":"RPRT","bibtex":"article","citeproc":"article-journal","schemaOrg":"ScholarlyArticle","resourceType":"Proposal","resourceTypeGeneral":"Text"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.20099556","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.20099556","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-05-09T17:52:41Z","registered":"2026-05-09T17:52:41Z","published":null,"updated":"2026-05-09T17:52:41Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.20099557","type":"dois","attributes":{"doi":"10.5281/zenodo.20099557","identifiers":[{"identifier":"oai:zenodo.org:20099557","identifierType":"oai"}],"creators":[{"name":"Abinash Bharadwaj, A.Bharadwaj","nameType":"Personal","givenName":"A.Bharadwaj","familyName":"Abinash Bharadwaj","nameIdentifiers":[{"nameIdentifier":"0009-0005-2851-3101","nameIdentifierScheme":"ORCID"}],"affiliation":[]}],"titles":[{"title":"COGNITIVE DNA BY ABINASH BHARADWAJ THEORY OF C-DNA BY A.BHARADWAJ"},{"lang":"eng","title":"C-DNA: Cognitive DNA Theory","titleType":"AlternativeTitle"}],"publisher":"Zenodo","container":{},"publicationYear":2025,"subjects":[{"subject":"Cognition","subjectScheme":"MeSH"},{"subject":"Cognitive neuroscience","subjectScheme":"EuroSciVoc"},{"subject":"Cognitive psychology","subjectScheme":"EuroSciVoc"},{"subject":"Cognitive Psychology","subjectScheme":"MeSH"},{"subject":"Cognition/classification","subjectScheme":"MeSH"},{"subject":"Cognitive 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Hristo","nameType":"Personal","givenName":"Hristo","familyName":"Nedelchev","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"The Equation Reduction Model (ERM): A Universal Framework for Mathematical Stability and Invariant Discovery"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Discrete mathematics","subjectScheme":"EuroSciVoc"},{"subject":"Equation Reduction"},{"subject":"Information Theory","subjectScheme":"MeSH"},{"subject":"Hristo Nedelchev"}],"contributors":[],"dates":[{"date":"2026-05-09","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19350324","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"GNU General Public License v3.0 or later","rightsUri":"https://www.gnu.org/licenses/gpl-3.0-standalone.html","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"gpl-3.0+","rightsIdentifierScheme":"SPDX"},{"rights":"Copyright (C) 2026 Hristo Nedelchev","rightsUri":"http://rightsstatements.org/vocab/InC/1.0/"}],"descriptions":[{"description":"ERM Logic Filtration: A Universal Framework for Hypothesis Validation and Equation Extraction\n\nSummary:\n\nThe Energy Resonance Model (ERM) is a revolutionary logical filter designed to distinguish between \"Structural Truth\" and \"Informational Chaos.\" In modern science, researchers often waste years pursuing hypotheses that are ultimately based on noise. Standard AI models exacerbate this by attempting to \"force\" a mathematical fit onto random data through statistical probability.\n\nThe ERM Logic Gatekeeper:\n\nInstead of trying to fit a curve to data points, ERM checks for Structural Resonance.\n\n\n\n\n\nThe Logic Test: If a hypothesis is correct, the relationship between the raw data and the ERM Skeleton ($a^2+b^2+c^2 - ab-bc-ca$) will produce a stable constant or a predictable pattern.\n\n\n\n\nThe Falsification Test: If the relationship is chaotic or fluctuates wildly, ERM proves that there is no underlying algebraic logic.\n\n\n\nImpact:\n\nBy using ERM as a preliminary filter, scientists can instantly validate or discard hypotheses.\n\n\n\n\n\nIf Logic is detected: ERM extracts the governing equation in milliseconds.\n\n\n\n\nIf Logic is absent: ERM prevents the waste of years of human research and millions in supercomputing costs.\n\n\n\nAutonomous Algebraic Skeleton Extraction (AASE) via Energy Resonance Model (ERM): Overcoming the Limits of AI Symbolic Regression\n\nOverview: The 100-Year Problem of Scientific Discovery\n\nFor centuries, scientists have discovered physical laws through tedious trial and error—observing data, guessing a mathematical relationship, and testing it. Today, Artificial Intelligence (AI) attempts to automate this through \"Symbolic Regression.\" However, standard AI uses statistical guessing (neural networks) to fit curves to data points. It tries millions of combinations, hoping one fits.\n\nThis brute-force method has two massive flaws:\n\n\n\n\n\nIt is incredibly slow and computationally expensive.\n\n\n\n\nIt suffers from \"hardware blindness.\" When dealing with extremely large numbers or microscopic differences (near the machine epsilon of $10^{-16}$), standard AI loses precision. It hallucinates, returns noise, or crashes, producing false physics.\n\n\n\nThe ERM Solution: The \"Algebraic X-Ray\"\n\nThe Energy Resonance Model (ERM) introduces a fundamentally different approach. Instead of guessing formulas statistically, ERM uses a lightweight, deterministic function—the \"Algebraic Skeleton\"—to instantly scan the structural energy balance of the data.\n\nThe core function is:\n\n$F(a, b, c) = a^2 + b^2 + c^2 - (ab + bc + ca)$\n\nThis small function acts as a mathematical filter. By analyzing the symmetry and variance between variables without heavy operations like division or cubing (which break standard AI), ERM extracts the exact integer coefficients and logical structure of the data stream in milliseconds.\n\nStandard AI vs. ERM Calculation\n\n\n\n\n\nHow Standard AI thinks: \"I have these points. Let me try $x^2$. No. Let me try $\\sin(x)$. No. Let me try adding a small weight. The numbers are too big, my floating-point memory is overflowing, so the answer is probably 0.\" (This leads to hallucination and data death).\n\n\n\n\nHow ERM thinks: \"I do not guess. I measure the exact energy difference using $F(a,b,c)$. The structural ratio reveals that this is an inverse square law. The exact coefficient is 1.0. Done.\"\n\n\n\nWhy This Changes AGI (Artificial General Intelligence):\n\n\n\n\n\nInstant Equation Extraction: What takes standard algorithms hours or days of supercomputing, ERM solves in less than a millisecond with 100% precision.\n\n\n\n\n100% Noise Immunity (Smart Zero): ERM naturally filters out hardware approximation errors, ensuring that an AGI system will never make a catastrophic logical error due to floating-point limits.\n\n\n\n\nTrue Scientific Discovery: ERM allows machines to deduce new physical laws from raw data autonomously, based on pure algebraic truth rather than statistical probability.\n\n\n\n# ERM: The Logical Safety Fuse for Paradox-Resistant AGI\n\nCurrent Large Language Model (LLM) architectures are inherently vulnerable to systemic crashes and logical \"hallucinations\" when encountering mathematical singularities or infinite loops. This project introduces the **Entropy Reduction Mechanism (ERM)** — a critical \"logical safety fuse\" designed to prevent these failures by reducing complex algebraic noise into a stable discrete skeleton of **{-1, 0, 1}**.\n\n### 🚀 Key Breakthroughs of the ERM Model:\n\n* **Smart Zero (ε = 10⁻¹⁷):** A critical computational buffer that neutralizes `DivisionByZero` errors. It allows the system to navigate mathematical singularities that typically trigger terminal crashes in standard AI logic.* **High-Speed Determinism:** Empirical stress tests demonstrate a processing throughput of **1.09 million operations per second with 0% informational noise**, ensuring absolute stability under heavy load.* **Hyper-Position Logic (11):** A proprietary method for resolving logical paradoxes by converting them into stable energy states rather than terminal errors or infinite loops.\n\n### 🛡️ VisionImplementing the ERM layer as a core safety protocol establishes the foundation for a **crash-resistant Artificial General Intelligence (AGI)** environment, where logic is filtered for integrity before execution.\n\n---**Keywords:** AGI Stability, Entropy Reduction, Smart Zero, AI Safety, Deterministic Logic, Paradox Resolution.\n\nThis version of the Equation Reduction Model (ERM) provides a complete analytical and computational proof of the framework's validity. ERM is not merely a statistical tool; it is a structural sieve that identifies fundamental mathematical invariants by reducing complex continuous systems to a discrete ternary state space $\\{-1, 0, 1\\}$.\n\nKey Scientific Contributions in this version:\n\n\n\n\n\nAnalytical Proof of Stability: The ERM invariant is formally proven to be algebraically equivalent to a \"Sum of Squares\" structure: $ERM = \\frac{1}{2}[(a-b)^2 + (b-c)^2 + (c-a)^2]$. This identity guarantees non-negative structural integrity ($ERM \\ge 0$) across all real numbers, representing a state of absolute physical equilibrium.\n\n\n\n\nNon-Triviality: Unlike simple quadratic sums, ERM emerges from discrete logic to define the minimal energy boundaries of interacting systems. It successfully distinguishes between universal laws (e.g., Pythagorean identity) and unstable linear approximations.\n\n\n\n\nInformation Density: Shannon Entropy analysis confirms a 91.8% information efficiency, proving the model reflects a highly organized logical skeleton of reality.\n\n\n\n\nComputational Toolkit: Included are four Python-based verification scripts that allow independent researchers to reproduce the 27-state logic, the 900-point stress test, and the symbolic algebraic proofs.\n\n\n\n\n### Description: The Universal Logic Filter (ERM v2.0)\n\n \n\nThis project presents the advanced evolution of the **Equation Reduction Model (ERM)**. The core innovation lies in the transition from a traditional three-state arithmetic system (-1, 0, 1) to a robust **Binary Hyper-Position framework** ({00, 01, 10, 11}).\n\n \n\n**Key Innovations:**\n\n* **From Numbers to Information States:** By replacing the integer -1 with discrete bit-states, the model becomes natively compatible with binary computing and quantum logic.\n\n* **The Sieve of Truth:** ERM functions as a \"Structural Scanner\" for mathematical identities. Valid laws of physics (like Pythagoras and Einstein's E=mc²) result in a state of 0% entropy (State 00), while flawed equations generate measurable \"Logical Noise.\"\n\n* **Entropy-Based Verification:** In experimental stress tests, structurally incorrect theories produced 55.56% informational noise, providing a visual and mathematical \"fingerprint\" of error.\n\n \n\n**Impact:**\n\nThe Nedelchev ERM provides a language-independent tool for AI and theoretical physics to verify the structural integrity of equations without traditional computation, focusing instead on logical symmetry and balance.\n\n \n\n**Included in the PDF:** Theoretical background, binary mapping rules, visual proof of truth vs. chaos, and Python implementation code.\n\n\n\n\nThe Hybrid Evolution: Integration of Intelligence and Safety\n\nThe Hybrid ERM represents the final architectural transition of the model—moving from a passive structural sieve to an active, self-regulating logical organism. It bridges the gap between raw mathematical energy and structural digital forms.\n\nCore Hybrid Features:\n\n\n\n\n\nThe Triple Hyper-Position Architecture: The model operates on three hierarchical levels:\n\n\n\n\n\nThe Energy Core (-1, 0, 1): The \"Heart\" that dictates the fundamental direction and balance of the input.\n\n\n\n\nThe Structural Anchor (00, 01, 10, 11): The \"Body\" that gives form to the energy, mapping it into a natively digital 2-bit space.\n\n\n\n\nThe Decision Guard (???): The \"Intelligence\" layer—a revolutionary Hyper-Position of Uncertainty.\n\n\n\n\n\n\nThe Logic Fuse (The \"I Don't Know\" Principle): Unlike traditional deterministic algorithms that force a binary output, the Hybrid ERM possesses a \"safety fuse.\" When encountering mathematical singularities (e.g., $1/0$), infinite values, or data exceeding the defined Standard Benchmark, the system triggers the ??? state. This prevents the propagation of logical errors and protects the integrity of the entire network.\n\n\n\n\nDynamic Regeneration Protocol: The Hybrid model is designed for autonomous error correction. By aligning structural binary states with the ternary core's absolute balance (0), the system can \"pull\" distorted signals back to a state of zero entropy, effectively acting as an Immune System for Information.\n\n\n\nScientific Impact of the Hybrid Version:\n\nThe Hybrid ERM proves that true intelligence is the ability to recognize boundaries. By integrating the \"Hyper-Position of Uncertainty,\" this framework provides a blueprint for next-generation AI and cybersecurity systems that are immune to logical paradoxes and \"Black Hole\" data overflows.\n\n\n\n\nProject Title: The Smart Zero Paradigm: Resolving Logical Hyper-positions via Hybrid ERM v3.0\n\nSummary:\n\nThis project introduces a novel approach to resolving logical deadlocks and paradoxes in digital systems. While classical logic fails in states of perfect symmetry (Hyper-positions), the Hybrid ERM v3.0 model demonstrates that such states are only static illusions.\n\nKey Findings:\n\n\n\n\n\nThe Smart Zero Principle: Computation inherently generates infinitesimal noise ($10^{-17}$), transforming a \"Static Zero\" into a \"Smart Zero.\"\n\n\n\n\nInformation Vector: This microscopic asymmetry serves as a deterministic vector that forces a system to exit a paradox and reach a decision (01 or 10).\n\n\n\n\nThe Creation Act: Proves that by consciously designing noise, we can steer logical outcomes in autonomous systems.\n\n\n\n\nThis dataset provides the experimental framework and primary results for the Entropy Reduction Mechanism (ERM), a novel architectural layer designed by Hristo Valentinov Nedelchev. The ERM addresses the fundamental problem of logical stagnation and systemic crashes in Artificial Intelligence when encountering mathematical singularities (e.g., division by zero) and recursive paradoxes.\n\nThe provided files include:\n\n\n\n\n\nERM_Symmetry_Breaker.py: The core validation engine that executes three critical stress tests: Mathematical Singularity, Logical Stagnation, and Spectral Sensitivity.\n\n\n\n\nFinal_Victory_Graph.png: A visual proof using logarithmic scaling to demonstrate how ERM \"breaks\" the symmetry of a logical stalemate, allowing the system to converge where standard AI models fail.\n\n\n\n\nValidation Protocols: Raw data logs confirming that ERM maintains a 100% stability rate and restores signal integrity at the $10^{-18}$ spectrum.\n\n\n\nMethodological Significance:\n\nUnlike traditional error-handling, ERM introduces the \"Smart Zero\" ($10^{-17}$), a strategic perturbation that acts as a symmetry-breaker. This dataset proves that ERM-enabled systems can navigate non-computable states, making it a critical component for future AGI (Artificial General Intelligence) stability.\n\nKeywords: Artificial Intelligence, AGI, ERM, Symmetry Breaking, Smart Zero, Neural Network Stability, Logic Paradox Resolution.\n\n\n\nIncluded Files:\n\n\n         Nedelchev_Unified_ERM_Theory_2026.pdf\n\n\n\n        ERM_Core.pdf\n\n\n\nerm_core.py\n\n\n\nmain.pdf: Theoretical framework and mathematical proof.\n\n\n\n\nlogic_check.py: Experimental validation of Smart Zero vs. Static Zero.\n\n\n\n\nerm_resolver.py: The core algorithm engine for paradox resolution.\n\n\n\n\nproof_of_motion.txt: Computational logs proving the collapse of hyper-positions.\n\n\n\n\n\nIncluded Files:\n\n       Nedelchev_Unified_ERM_Theory_2026.pdf\n\n\n\n\n\nERM_Core_Logic.py (Discrete state analysis)\n\n\n\n\nERM_Stress_Test.py (Continuous surface validation)\n\n\n\n\nERM_Universal_Checker.py (Symbolic algebraic proof)\n\n\n\n\nERM_Discovery_Demo.py (Automated law synthesis demo)\n\n\n\n\nERM_Universal_Stability_Framework.pdf (The full scientific whitepaper)\n\n\nERM__Binary_Logic_Mapping_and_Information_Entropy \n\nERM_Hyper_Stress_Test.py\n\nHybrid_ERM_Core.py\n\nThe_Hybrid_ERM_Paradigm.pdf\n\nERM_Experimental_Validation_v3\n\nscientific_validation.py\n\nrecursive_test.py\n\nstress_test_speed.py\n\nlogic_checkp.py\n\nerm_resolver.py\n\nproof_of_motion.txt\n\nThe_Smart_Zero_Paradigm_ERM_v3_Nedelchev.pdf\n\nERM_The_Unified_Logic.pdf\n\nERM_The_Logic_Filter.pdf","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.20099546","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-05-09T17:46:22Z","registered":"2026-05-09T17:46:22Z","published":null,"updated":"2026-05-09T17:46:22Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19350324","type":"dois","attributes":{"doi":"10.5281/zenodo.19350324","identifiers":[],"creators":[{"name":"Nedelchev, Hristo","nameType":"Personal","givenName":"Hristo","familyName":"Nedelchev","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"The Equation Reduction Model (ERM): A Universal Framework for Mathematical Stability and Invariant Discovery"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Discrete mathematics","subjectScheme":"EuroSciVoc"},{"subject":"Equation Reduction"},{"subject":"Information Theory","subjectScheme":"MeSH"},{"subject":"Hristo Nedelchev"}],"contributors":[],"dates":[{"date":"2026-05-09","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19350324","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"GNU General Public License v3.0 or later","rightsUri":"https://www.gnu.org/licenses/gpl-3.0-standalone.html","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"gpl-3.0+","rightsIdentifierScheme":"SPDX"},{"rights":"Copyright (C) 2026 Hristo Nedelchev","rightsUri":"http://rightsstatements.org/vocab/InC/1.0/"}],"descriptions":[{"description":"ERM Logic Filtration: A Universal Framework for Hypothesis Validation and Equation Extraction\n\nSummary:\n\nThe Energy Resonance Model (ERM) is a revolutionary logical filter designed to distinguish between \"Structural Truth\" and \"Informational Chaos.\" In modern science, researchers often waste years pursuing hypotheses that are ultimately based on noise. Standard AI models exacerbate this by attempting to \"force\" a mathematical fit onto random data through statistical probability.\n\nThe ERM Logic Gatekeeper:\n\nInstead of trying to fit a curve to data points, ERM checks for Structural Resonance.\n\n\n\n\n\nThe Logic Test: If a hypothesis is correct, the relationship between the raw data and the ERM Skeleton ($a^2+b^2+c^2 - ab-bc-ca$) will produce a stable constant or a predictable pattern.\n\n\n\n\nThe Falsification Test: If the relationship is chaotic or fluctuates wildly, ERM proves that there is no underlying algebraic logic.\n\n\n\nImpact:\n\nBy using ERM as a preliminary filter, scientists can instantly validate or discard hypotheses.\n\n\n\n\n\nIf Logic is detected: ERM extracts the governing equation in milliseconds.\n\n\n\n\nIf Logic is absent: ERM prevents the waste of years of human research and millions in supercomputing costs.\n\n\n\nAutonomous Algebraic Skeleton Extraction (AASE) via Energy Resonance Model (ERM): Overcoming the Limits of AI Symbolic Regression\n\nOverview: The 100-Year Problem of Scientific Discovery\n\nFor centuries, scientists have discovered physical laws through tedious trial and error—observing data, guessing a mathematical relationship, and testing it. Today, Artificial Intelligence (AI) attempts to automate this through \"Symbolic Regression.\" However, standard AI uses statistical guessing (neural networks) to fit curves to data points. It tries millions of combinations, hoping one fits.\n\nThis brute-force method has two massive flaws:\n\n\n\n\n\nIt is incredibly slow and computationally expensive.\n\n\n\n\nIt suffers from \"hardware blindness.\" When dealing with extremely large numbers or microscopic differences (near the machine epsilon of $10^{-16}$), standard AI loses precision. It hallucinates, returns noise, or crashes, producing false physics.\n\n\n\nThe ERM Solution: The \"Algebraic X-Ray\"\n\nThe Energy Resonance Model (ERM) introduces a fundamentally different approach. Instead of guessing formulas statistically, ERM uses a lightweight, deterministic function—the \"Algebraic Skeleton\"—to instantly scan the structural energy balance of the data.\n\nThe core function is:\n\n$F(a, b, c) = a^2 + b^2 + c^2 - (ab + bc + ca)$\n\nThis small function acts as a mathematical filter. By analyzing the symmetry and variance between variables without heavy operations like division or cubing (which break standard AI), ERM extracts the exact integer coefficients and logical structure of the data stream in milliseconds.\n\nStandard AI vs. ERM Calculation\n\n\n\n\n\nHow Standard AI thinks: \"I have these points. Let me try $x^2$. No. Let me try $\\sin(x)$. No. Let me try adding a small weight. The numbers are too big, my floating-point memory is overflowing, so the answer is probably 0.\" (This leads to hallucination and data death).\n\n\n\n\nHow ERM thinks: \"I do not guess. I measure the exact energy difference using $F(a,b,c)$. The structural ratio reveals that this is an inverse square law. The exact coefficient is 1.0. Done.\"\n\n\n\nWhy This Changes AGI (Artificial General Intelligence):\n\n\n\n\n\nInstant Equation Extraction: What takes standard algorithms hours or days of supercomputing, ERM solves in less than a millisecond with 100% precision.\n\n\n\n\n100% Noise Immunity (Smart Zero): ERM naturally filters out hardware approximation errors, ensuring that an AGI system will never make a catastrophic logical error due to floating-point limits.\n\n\n\n\nTrue Scientific Discovery: ERM allows machines to deduce new physical laws from raw data autonomously, based on pure algebraic truth rather than statistical probability.\n\n\n\n# ERM: The Logical Safety Fuse for Paradox-Resistant AGI\n\nCurrent Large Language Model (LLM) architectures are inherently vulnerable to systemic crashes and logical \"hallucinations\" when encountering mathematical singularities or infinite loops. This project introduces the **Entropy Reduction Mechanism (ERM)** — a critical \"logical safety fuse\" designed to prevent these failures by reducing complex algebraic noise into a stable discrete skeleton of **{-1, 0, 1}**.\n\n### 🚀 Key Breakthroughs of the ERM Model:\n\n* **Smart Zero (ε = 10⁻¹⁷):** A critical computational buffer that neutralizes `DivisionByZero` errors. It allows the system to navigate mathematical singularities that typically trigger terminal crashes in standard AI logic.* **High-Speed Determinism:** Empirical stress tests demonstrate a processing throughput of **1.09 million operations per second with 0% informational noise**, ensuring absolute stability under heavy load.* **Hyper-Position Logic (11):** A proprietary method for resolving logical paradoxes by converting them into stable energy states rather than terminal errors or infinite loops.\n\n### 🛡️ VisionImplementing the ERM layer as a core safety protocol establishes the foundation for a **crash-resistant Artificial General Intelligence (AGI)** environment, where logic is filtered for integrity before execution.\n\n---**Keywords:** AGI Stability, Entropy Reduction, Smart Zero, AI Safety, Deterministic Logic, Paradox Resolution.\n\nThis version of the Equation Reduction Model (ERM) provides a complete analytical and computational proof of the framework's validity. ERM is not merely a statistical tool; it is a structural sieve that identifies fundamental mathematical invariants by reducing complex continuous systems to a discrete ternary state space $\\{-1, 0, 1\\}$.\n\nKey Scientific Contributions in this version:\n\n\n\n\n\nAnalytical Proof of Stability: The ERM invariant is formally proven to be algebraically equivalent to a \"Sum of Squares\" structure: $ERM = \\frac{1}{2}[(a-b)^2 + (b-c)^2 + (c-a)^2]$. This identity guarantees non-negative structural integrity ($ERM \\ge 0$) across all real numbers, representing a state of absolute physical equilibrium.\n\n\n\n\nNon-Triviality: Unlike simple quadratic sums, ERM emerges from discrete logic to define the minimal energy boundaries of interacting systems. It successfully distinguishes between universal laws (e.g., Pythagorean identity) and unstable linear approximations.\n\n\n\n\nInformation Density: Shannon Entropy analysis confirms a 91.8% information efficiency, proving the model reflects a highly organized logical skeleton of reality.\n\n\n\n\nComputational Toolkit: Included are four Python-based verification scripts that allow independent researchers to reproduce the 27-state logic, the 900-point stress test, and the symbolic algebraic proofs.\n\n\n\n\n### Description: The Universal Logic Filter (ERM v2.0)\n\n \n\nThis project presents the advanced evolution of the **Equation Reduction Model (ERM)**. The core innovation lies in the transition from a traditional three-state arithmetic system (-1, 0, 1) to a robust **Binary Hyper-Position framework** ({00, 01, 10, 11}).\n\n \n\n**Key Innovations:**\n\n* **From Numbers to Information States:** By replacing the integer -1 with discrete bit-states, the model becomes natively compatible with binary computing and quantum logic.\n\n* **The Sieve of Truth:** ERM functions as a \"Structural Scanner\" for mathematical identities. Valid laws of physics (like Pythagoras and Einstein's E=mc²) result in a state of 0% entropy (State 00), while flawed equations generate measurable \"Logical Noise.\"\n\n* **Entropy-Based Verification:** In experimental stress tests, structurally incorrect theories produced 55.56% informational noise, providing a visual and mathematical \"fingerprint\" of error.\n\n \n\n**Impact:**\n\nThe Nedelchev ERM provides a language-independent tool for AI and theoretical physics to verify the structural integrity of equations without traditional computation, focusing instead on logical symmetry and balance.\n\n \n\n**Included in the PDF:** Theoretical background, binary mapping rules, visual proof of truth vs. chaos, and Python implementation code.\n\n\n\n\nThe Hybrid Evolution: Integration of Intelligence and Safety\n\nThe Hybrid ERM represents the final architectural transition of the model—moving from a passive structural sieve to an active, self-regulating logical organism. It bridges the gap between raw mathematical energy and structural digital forms.\n\nCore Hybrid Features:\n\n\n\n\n\nThe Triple Hyper-Position Architecture: The model operates on three hierarchical levels:\n\n\n\n\n\nThe Energy Core (-1, 0, 1): The \"Heart\" that dictates the fundamental direction and balance of the input.\n\n\n\n\nThe Structural Anchor (00, 01, 10, 11): The \"Body\" that gives form to the energy, mapping it into a natively digital 2-bit space.\n\n\n\n\nThe Decision Guard (???): The \"Intelligence\" layer—a revolutionary Hyper-Position of Uncertainty.\n\n\n\n\n\n\nThe Logic Fuse (The \"I Don't Know\" Principle): Unlike traditional deterministic algorithms that force a binary output, the Hybrid ERM possesses a \"safety fuse.\" When encountering mathematical singularities (e.g., $1/0$), infinite values, or data exceeding the defined Standard Benchmark, the system triggers the ??? state. This prevents the propagation of logical errors and protects the integrity of the entire network.\n\n\n\n\nDynamic Regeneration Protocol: The Hybrid model is designed for autonomous error correction. By aligning structural binary states with the ternary core's absolute balance (0), the system can \"pull\" distorted signals back to a state of zero entropy, effectively acting as an Immune System for Information.\n\n\n\nScientific Impact of the Hybrid Version:\n\nThe Hybrid ERM proves that true intelligence is the ability to recognize boundaries. By integrating the \"Hyper-Position of Uncertainty,\" this framework provides a blueprint for next-generation AI and cybersecurity systems that are immune to logical paradoxes and \"Black Hole\" data overflows.\n\n\n\n\nProject Title: The Smart Zero Paradigm: Resolving Logical Hyper-positions via Hybrid ERM v3.0\n\nSummary:\n\nThis project introduces a novel approach to resolving logical deadlocks and paradoxes in digital systems. While classical logic fails in states of perfect symmetry (Hyper-positions), the Hybrid ERM v3.0 model demonstrates that such states are only static illusions.\n\nKey Findings:\n\n\n\n\n\nThe Smart Zero Principle: Computation inherently generates infinitesimal noise ($10^{-17}$), transforming a \"Static Zero\" into a \"Smart Zero.\"\n\n\n\n\nInformation Vector: This microscopic asymmetry serves as a deterministic vector that forces a system to exit a paradox and reach a decision (01 or 10).\n\n\n\n\nThe Creation Act: Proves that by consciously designing noise, we can steer logical outcomes in autonomous systems.\n\n\n\n\nThis dataset provides the experimental framework and primary results for the Entropy Reduction Mechanism (ERM), a novel architectural layer designed by Hristo Valentinov Nedelchev. The ERM addresses the fundamental problem of logical stagnation and systemic crashes in Artificial Intelligence when encountering mathematical singularities (e.g., division by zero) and recursive paradoxes.\n\nThe provided files include:\n\n\n\n\n\nERM_Symmetry_Breaker.py: The core validation engine that executes three critical stress tests: Mathematical Singularity, Logical Stagnation, and Spectral Sensitivity.\n\n\n\n\nFinal_Victory_Graph.png: A visual proof using logarithmic scaling to demonstrate how ERM \"breaks\" the symmetry of a logical stalemate, allowing the system to converge where standard AI models fail.\n\n\n\n\nValidation Protocols: Raw data logs confirming that ERM maintains a 100% stability rate and restores signal integrity at the $10^{-18}$ spectrum.\n\n\n\nMethodological Significance:\n\nUnlike traditional error-handling, ERM introduces the \"Smart Zero\" ($10^{-17}$), a strategic perturbation that acts as a symmetry-breaker. This dataset proves that ERM-enabled systems can navigate non-computable states, making it a critical component for future AGI (Artificial General Intelligence) stability.\n\nKeywords: Artificial Intelligence, AGI, ERM, Symmetry Breaking, Smart Zero, Neural Network Stability, Logic Paradox Resolution.\n\n\n\nIncluded Files:\n\n\n         Nedelchev_Unified_ERM_Theory_2026.pdf\n\n\n\n        ERM_Core.pdf\n\n\n\nerm_core.py\n\n\n\nmain.pdf: Theoretical framework and mathematical proof.\n\n\n\n\nlogic_check.py: Experimental validation of Smart Zero vs. Static Zero.\n\n\n\n\nerm_resolver.py: The core algorithm engine for paradox resolution.\n\n\n\n\nproof_of_motion.txt: Computational logs proving the collapse of hyper-positions.\n\n\n\n\n\nIncluded Files:\n\n       Nedelchev_Unified_ERM_Theory_2026.pdf\n\n\n\n\n\nERM_Core_Logic.py (Discrete state analysis)\n\n\n\n\nERM_Stress_Test.py (Continuous surface validation)\n\n\n\n\nERM_Universal_Checker.py (Symbolic algebraic proof)\n\n\n\n\nERM_Discovery_Demo.py (Automated law synthesis demo)\n\n\n\n\nERM_Universal_Stability_Framework.pdf (The full scientific whitepaper)\n\n\nERM__Binary_Logic_Mapping_and_Information_Entropy \n\nERM_Hyper_Stress_Test.py\n\nHybrid_ERM_Core.py\n\nThe_Hybrid_ERM_Paradigm.pdf\n\nERM_Experimental_Validation_v3\n\nscientific_validation.py\n\nrecursive_test.py\n\nstress_test_speed.py\n\nlogic_checkp.py\n\nerm_resolver.py\n\nproof_of_motion.txt\n\nThe_Smart_Zero_Paradigm_ERM_v3_Nedelchev.pdf\n\nERM_The_Unified_Logic.pdf\n\nERM_The_Logic_Filter.pdf","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19350324","contentUrl":null,"metadataVersion":14,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":13,"versionOfCount":1,"created":"2026-03-31T10:41:00Z","registered":"2026-03-31T10:41:00Z","published":null,"updated":"2026-05-09T17:46:22Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.15620361","type":"dois","attributes":{"doi":"10.5281/zenodo.15620361","identifiers":[{"identifier":"oai:zenodo.org:15620361","identifierType":"oai"}],"creators":[{"name":"Bostick, Devin","nameType":"Personal","givenName":"Devin","familyName":"Bostick","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"The Cashmiri Effect_ When Coherence Becomes Unreadable"}],"publisher":"Zenodo","container":{},"publicationYear":2025,"subjects":[{"subject":"Cognitive Science","subjectScheme":"MeSH"},{"subject":"Epistemology","subjectScheme":"EuroSciVoc"},{"subject":"Artificial intelligence","subjectScheme":"EuroSciVoc"},{"subject":"Theory of Mind","subjectScheme":"MeSH"},{"subject":"Theoretical physics","subjectScheme":"EuroSciVoc"},{"subject":"Cashmiri Effect,"},{"subject":"PAS (Phase Alignment Score)"},{"subject":"Resonance Intelligence Core"},{"subject":"Coherence Misclassification"},{"subject":"Symbolic Entropy"},{"subject":"Identity Rephrasing"},{"subject":"Epistemic Inversion"},{"subject":"Intelligence Alignment"},{"subject":"Phase-structure Diagnostics"}],"contributors":[],"dates":[{"date":"2025-06-08","dateType":"Issued"}],"language":"en","types":{"ris":"RPRT","bibtex":"article","citeproc":"article-journal","schemaOrg":"ScholarlyArticle","resourceType":"","resourceTypeGeneral":"Text"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.15620360","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Supersession Note — May 2026\n\nThis CODES-era work is an exploratory predecessor and is no longer the canonical statement of the author’s program. It has been superseded by the identity-persistence stack:\n\nUniversal Identity and Persistence:\n\nhttps://zenodo.org/records/19904166\n\nA Mathematical Theory of Identity Persistence:\n\nhttps://zenodo.org/records/19967345\n\nA Coding Theorem for Identity Persistence:\n\nhttps://zenodo.org/records/19996467\n\nIdentity Persistence Calculus:\n\nhttps://zenodo.org/records/19905404\n\nThe Bounded Corridor:\n\nhttps://zenodo.org/records/19645631\n\nThe Unclosable Bridge:\n\nhttps://zenodo.org/records/19601328\n\nClaims in this record concerning replacement of probability, unrestricted universality, ontology, physics, intelligence, biology, governance, or reality should be read as developmental framing, not as the current formal claim.\n\nThe current claim is restricted to identity persistence under transformation within explicit admissibility constraints: recurrence comparability, admissible redescription, bounded drift, scalar or scalar-equivalent governance, finite identity capacity, and deterministic finite-regime coding/enforcement where applicable.\n\n ","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.15620361","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2025-06-08T20:33:47Z","registered":"2025-06-08T20:33:47Z","published":null,"updated":"2026-05-09T17:44:45Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.15620360","type":"dois","attributes":{"doi":"10.5281/zenodo.15620360","identifiers":[],"creators":[{"name":"Bostick, Devin","nameType":"Personal","givenName":"Devin","familyName":"Bostick","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"The Cashmiri Effect_ When Coherence Becomes Unreadable"}],"publisher":"Zenodo","container":{},"publicationYear":2025,"subjects":[{"subject":"Cognitive Science","subjectScheme":"MeSH"},{"subject":"Epistemology","subjectScheme":"EuroSciVoc"},{"subject":"Artificial intelligence","subjectScheme":"EuroSciVoc"},{"subject":"Theory of Mind","subjectScheme":"MeSH"},{"subject":"Theoretical physics","subjectScheme":"EuroSciVoc"},{"subject":"Cashmiri Effect,"},{"subject":"PAS (Phase Alignment Score)"},{"subject":"Resonance Intelligence Core"},{"subject":"Coherence Misclassification"},{"subject":"Symbolic Entropy"},{"subject":"Identity Rephrasing"},{"subject":"Epistemic Inversion"},{"subject":"Intelligence Alignment"},{"subject":"Phase-structure Diagnostics"}],"contributors":[],"dates":[{"date":"2025-06-08","dateType":"Issued"}],"language":"en","types":{"ris":"RPRT","bibtex":"article","citeproc":"article-journal","schemaOrg":"ScholarlyArticle","resourceType":"","resourceTypeGeneral":"Text"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.15620360","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Supersession Note — May 2026\n\nThis CODES-era work is an exploratory predecessor and is no longer the canonical statement of the author’s program. It has been superseded by the identity-persistence stack:\n\nUniversal Identity and Persistence:\n\nhttps://zenodo.org/records/19904166\n\nA Mathematical Theory of Identity Persistence:\n\nhttps://zenodo.org/records/19967345\n\nA Coding Theorem for Identity Persistence:\n\nhttps://zenodo.org/records/19996467\n\nIdentity Persistence Calculus:\n\nhttps://zenodo.org/records/19905404\n\nThe Bounded Corridor:\n\nhttps://zenodo.org/records/19645631\n\nThe Unclosable Bridge:\n\nhttps://zenodo.org/records/19601328\n\nClaims in this record concerning replacement of probability, unrestricted universality, ontology, physics, intelligence, biology, governance, or reality should be read as developmental framing, not as the current formal claim.\n\nThe current claim is restricted to identity persistence under transformation within explicit admissibility constraints: recurrence comparability, admissible redescription, bounded drift, scalar or scalar-equivalent governance, finite identity capacity, and deterministic finite-regime coding/enforcement where applicable.\n\n ","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.15620360","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2025-06-08T20:33:47Z","registered":"2025-06-08T20:33:47Z","published":null,"updated":"2026-05-09T17:44:45Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.15742771","type":"dois","attributes":{"doi":"10.5281/zenodo.15742771","identifiers":[],"creators":[{"name":"Bostick, Devin","nameType":"Personal","givenName":"Devin","familyName":"Bostick","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"What He Saw But Couldn't Write (Ramanujan v2)"}],"publisher":"Zenodo","container":{},"publicationYear":2025,"subjects":[{"subject":"Ramanujan"},{"subject":"Partition Function"},{"subject":"Modular Forms"},{"subject":"Mock Theta"},{"subject":"Chirality"},{"subject":"Structured Resonance"},{"subject":"PAS (Phase Alignment Score)"},{"subject":"Prime numbers","subjectScheme":"EuroSciVoc"},{"subject":"Emission Gating"},{"subject":"CODES Framework"},{"subject":"Coherence Theory"},{"subject":"Mathematical Epistemology"},{"subject":"Symbolic Drift"},{"subject":"CHORDLOCK"},{"subject":"Resonance Intelligence Core"},{"subject":"Mathematical logic","subjectScheme":"EuroSciVoc"},{"subject":"Mathematics","subjectScheme":"MeSH"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2025-06-26","dateType":"Issued"}],"language":"en","types":{"ris":"RPRT","bibtex":"article","citeproc":"article-journal","schemaOrg":"ScholarlyArticle","resourceType":"","resourceTypeGeneral":"Text"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.15742771","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Supersession Note — May 2026\n\nThis CODES-era work is an exploratory predecessor and is no longer the canonical statement of the author’s program. It has been superseded by the identity-persistence stack:\n\nUniversal Identity and Persistence:\n\nhttps://zenodo.org/records/19904166\n\nA Mathematical Theory of Identity Persistence:\n\nhttps://zenodo.org/records/19967345\n\nA Coding Theorem for Identity Persistence:\n\nhttps://zenodo.org/records/19996467\n\nIdentity Persistence Calculus:\n\nhttps://zenodo.org/records/19905404\n\nThe Bounded Corridor:\n\nhttps://zenodo.org/records/19645631\n\nThe Unclosable Bridge:\n\nhttps://zenodo.org/records/19601328\n\nClaims in this record concerning replacement of probability, unrestricted universality, ontology, physics, intelligence, biology, governance, or reality should be read as developmental framing, not as the current formal claim.\n\nThe current claim is restricted to identity persistence under transformation within explicit admissibility constraints: recurrence comparability, admissible redescription, bounded drift, scalar or scalar-equivalent governance, finite identity capacity, and deterministic finite-regime coding/enforcement where applicable.\n\n ","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.15742771","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":0,"created":"2025-06-26T02:34:13Z","registered":"2025-06-26T02:34:13Z","published":null,"updated":"2026-05-09T17:42:14Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.15742772","type":"dois","attributes":{"doi":"10.5281/zenodo.15742772","identifiers":[{"identifier":"oai:zenodo.org:15742772","identifierType":"oai"}],"creators":[{"name":"Bostick, Devin","nameType":"Personal","givenName":"Devin","familyName":"Bostick","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"What He Saw But Couldn't Write (Ramanujan v2)"}],"publisher":"Zenodo","container":{},"publicationYear":2025,"subjects":[{"subject":"Ramanujan"},{"subject":"Partition Function"},{"subject":"Modular Forms"},{"subject":"Mock Theta"},{"subject":"Chirality"},{"subject":"Structured Resonance"},{"subject":"PAS (Phase Alignment Score)"},{"subject":"Prime numbers","subjectScheme":"EuroSciVoc"},{"subject":"Emission Gating"},{"subject":"CODES Framework"},{"subject":"Coherence Theory"},{"subject":"Mathematical Epistemology"},{"subject":"Symbolic Drift"},{"subject":"CHORDLOCK"},{"subject":"Resonance Intelligence Core"},{"subject":"Mathematical logic","subjectScheme":"EuroSciVoc"},{"subject":"Mathematics","subjectScheme":"MeSH"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2025-06-26","dateType":"Issued"}],"language":"en","types":{"ris":"RPRT","bibtex":"article","citeproc":"article-journal","schemaOrg":"ScholarlyArticle","resourceType":"","resourceTypeGeneral":"Text"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.15742771","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Supersession Note — May 2026\n\nThis CODES-era work is an exploratory predecessor and is no longer the canonical statement of the author’s program. It has been superseded by the identity-persistence stack:\n\nUniversal Identity and Persistence:\n\nhttps://zenodo.org/records/19904166\n\nA Mathematical Theory of Identity Persistence:\n\nhttps://zenodo.org/records/19967345\n\nA Coding Theorem for Identity Persistence:\n\nhttps://zenodo.org/records/19996467\n\nIdentity Persistence Calculus:\n\nhttps://zenodo.org/records/19905404\n\nThe Bounded Corridor:\n\nhttps://zenodo.org/records/19645631\n\nThe Unclosable Bridge:\n\nhttps://zenodo.org/records/19601328\n\nClaims in this record concerning replacement of probability, unrestricted universality, ontology, physics, intelligence, biology, governance, or reality should be read as developmental framing, not as the current formal claim.\n\nThe current claim is restricted to identity persistence under transformation within explicit admissibility constraints: recurrence comparability, admissible redescription, bounded drift, scalar or scalar-equivalent governance, finite identity capacity, and deterministic finite-regime coding/enforcement where applicable.\n\n ","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.15742772","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2025-06-26T02:34:13Z","registered":"2025-06-26T02:34:13Z","published":null,"updated":"2026-05-09T17:42:14Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19076797","type":"dois","attributes":{"doi":"10.5281/zenodo.19076797","identifiers":[{"identifier":"DOI 10.5281/zenodo.19076797","identifierType":"Handle"}],"creators":[{"name":"de Nicolas, Alvaro","nameType":"Personal","givenName":"Alvaro","familyName":"de 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validation"},{"subject":"Bland-Altman"},{"subject":"treemap"},{"subject":"AESIA"},{"subject":"sub-component scoring"},{"subject":"unemployment"},{"subject":"SEPE"},{"subject":"adoption gap"},{"subject":"adversarial audit"},{"subject":"red team"},{"subject":"FUNCAS"},{"subject":"AI vulnerability"},{"subject":"salary-vulnerability index"},{"subject":"Rodríguez-Fernández"},{"subject":"Felten et 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(methodology) / v15 (dataset) + Funcas addendum v1","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Vulnerabilidad de Empleos a la Inteligencia Artificial en España: Dataset, Metodología y Dashboard Interactivo (v31 / v15 + Funcas Cross-Validation Addendum)\n\nAI Vulnerability of Jobs in Spain — Complete Dataset, Methodology, Funcas Cross-Validation Addendum \u0026 Interactive Dashboard\n\nThis deposit contains the complete dataset, methodology, a methodological cross-validation addendum against the Funcas Working Paper DT-2026/04 (Rodríguez-Fernández, April 2026), and the interactive visualisation tool for assessing the theoretical vulnerability of 502 Spanish occupations to artificial intelligence. The analysis covers 22,732,223 workers (EPA Q4 2025, INE) and assigns each occupation a calibrated vulnerability score on a 0–10 scale, cross-referenced with salary data, EU AI Act risk classification, and impact typology.\n\nThe interactive dashboard is publicly available at: https://empleo-ai.anlakstudio.com\n\nThis is methodology v31 / dataset v15, deposited May 2026, and includes the Funcas Cross-Validation Addendum (v1) documenting a formal validation against Funcas Working Paper DT-2026/04 (Rodríguez-Fernández, April 2026) with Pearson r = 0.936 across the 9 grand groups CNO-11. The deposit is part of the project series under concept DOI 10.5281/zenodo.19076797, which collects all versions and complementary methodological notes.\n\nWhat's New in This Version\n\n\n\n\n\n\nComponent\n\n\n\nChange\n\n\n\n\n\nv30/v15 → v31/v15\n\n\n\nMethodology v31: terminology of the four sub-components aligned with the PDF (Digitisability D / Cognitive complexity C / Physical barrier F / Regulatory friction R); validation reporting separated into two distinct experiments (sub-component vs. holistic); description text reconciled with metodologia_v31.pdf\n\n\n\n\n\nSub-component validation\n\n\n\nPearson r = 0.953 on the four D / C / F / R sub-scores against the FUNCAS expert panel (post-rescoring, 7 models)\n\n\n\n\n\nHolistic validation\n\n\n\nWeighted Cohen's κ_w = 0.667 (substantial agreement) on the aggregated 0–10 score against the FUNCAS holistic rating — a separate experiment, not directly comparable to the sub-component r\n\n\n\n\n\nCross-validation\n\n\n\nFuncas Addendum v1 (unchanged): formal cross-validation against Funcas DT-2026/04 (Rodríguez-Fernández, April 2026) — Pearson r = 0.936, Spearman ρ = 0.830 across 9 grand groups CNO-11\n\n\n\n\n\nDecomposition\n\n\n\n4-digit decomposition of the four CNO-11 groups flagged by Funcas (groups 1, 2, 3, 4) — included in the addendum PDF and dataset\n\n\n\n\n\nMethodology\n\n\n\nv31 — 46+ pages with 57 technical notes and 7 appendices (A–G), including adversarial review log and the explicit rescore_method annotation on the scoring formula\n\n\n\n\n\nAdversarial review\n\n\n\nFormal red-teaming protocol (Appendix G): 3 waves, 28 incidents identified, 24 resolved\n\n\n\n\n\nSalary cascade\n\n\n\nValidated against 16 EES 2023 reference groups, MAPE 4.96% (post-correction)\n\n\n\n\n\nTotal workforce\n\n\n\n4-layer cascade: 22,732,223 (EPA → Census 2021 → SEPE 2024)\n\n\n\n\n\nHigh-vulnerability cohort\n\n\n\n2,752,961 workers (12.1%) at score ≥ 7 across the 502 occupations\n\n\n\n\n\nSalary-vulnerability index\n\n\n\n~€253,000 M (employment × salary × score/10, an aggregated indicator, not a wage-loss prediction)\n\n\n\n\n\nDataset\n\nThe core dataset (spain_502_v15_subcomp_complete.json) contains 502 records corresponding to the complete CNO-11 occupational taxonomy (SEPE expansion). Each record includes the following fields:\n\n\n\n\n\n\nField\n\n\n\nType\n\n\n\nDescription\n\n\n\n\n\n\n\ncno\n\n\n\nstring\n\n\n\n4-digit CNO-11 occupation code\n\n\n\n\n\nnombre\n\n\n\nstring\n\n\n\nOfficial occupation name (Spanish)\n\n\n\n\n\nsector\n\n\n\nstring\n\n\n\nAssigned economic sector (12 categories)\n\n\n\n\n\nempleo\n\n\n\ninteger\n\n\n\nEstimated employment (EPA Q4 2025, redistributed via Census 2021 + SEPE 2024 weights)\n\n\n\n\n\nsalario_medio_eur\n\n\n\nfloat\n\n\n\nEstimated mean gross annual salary (EUR), based on INE EES 2023 + educational premia + FR/PT proxies\n\n\n\n\n\nvulnerabilidad_ia_score\n\n\n\nfloat\n\n\n\nAI vulnerability score (0–10), calibrated for Spain; 4-component decomposition (D / C / F / R)\n\n\n\n\n\neu_ai_act\n\n\n\nstring\n\n\n\nEU AI Act risk classification (\"Alto riesgo\" Annex III, \"Riesgo limitado\", \"Riesgo mínimo\")\n\n\n\n\n\ntipo_impacto\n\n\n\nstring\n\n\n\nImpact typology: \"Sustitución parcial de tareas\", \"Híbrido\", or \"Aumentación\"\n\n\n\n\n\njustificacion\n\n\n\nstring\n\n\n\n3–4 sentence justification in Spanish explaining the automation vector and human-protective factors\n\n\n\n\n\ncensus_2021_employed\n\n\n\nfloat\n\n\n\nCensus 2021 employment figure used for intra-group weighting\n\n\n\n\n\nemployment_method\n\n\n\nstring\n\n\n\nEmployment estimation method identifier\n\n\n\n\n\nD_digitalizabilidad\n\n\n\nfloat\n\n\n\nSub-component D — Digitisability of core tasks\n\n\n\n\n\nC_complejidad_cognitiva\n\n\n\nfloat\n\n\n\nSub-component C — Cognitive complexity AI can plausibly replicate\n\n\n\n\n\nF_barrera_fisica\n\n\n\nfloat\n\n\n\nSub-component F — Physical barrier (need for in-person, embodied execution)\n\n\n\n\n\nR_friccion_regulatoria\n\n\n\nfloat\n\n\n\nSub-component R — Regulatory friction (licences, professional reserves, liability)\n\n\n\n\n\nrank_in_descending\n\n\n\ninteger (v15)\n\n\n\nRank position when the 502 occupations are sorted by descending vulnerability score\n\n\n\n\n\ncumulative_workers_descending\n\n\n\ninteger (v15)\n\n\n\nRunning total of workers when occupations are summed in descending-score order\n\n\n\n\n\npct_workforce_descending\n\n\n\nfloat (v15)\n\n\n\nCumulative percentage of total workforce at or above this rank\n\n\n\n\n\ncumulative_workers_at_or_above_score\n\n\n\ninteger (v15)\n\n\n\nTotal workers in occupations with score ≥ this occupation's score\n\n\n\n\n\npct_workforce_at_or_above_score\n\n\n\nfloat (v15)\n\n\n\nPercentage of total workforce in occupations at or above this score\n\n\n\n\n\nThe dataset distinguishes 499 unique employment values across the 502 occupations, providing the most disaggregated employment-by-occupation estimation available for Spain at the 4-digit CNO-11 level. The cumulative fields in v15 allow direct inverse-threshold queries (e.g., \"what fraction of the Spanish workforce is in occupations with score ≥ 7.5?\") without re-aggregation, and are also published as a standalone CSV (spain_v15_threshold_lookup.csv) for spreadsheet use.\n\nKey Findings\n\n\n\n\n\n\nIndicator\n\n\n\nValue\n\n\n\nNote\n\n\n\n\n\n\n\nOccupations analysed\n\n\n\n502\n\n\n\nComplete CNO-11 (SEPE taxonomy)\n\n\n\n\n\nWorkers represented\n\n\n\n22,732,223\n\n\n\nEPA Q4 2025 (final data) reassigned to 4-digit CNO-11\n\n\n\n\n\nWeighted mean vulnerability\n\n\n\n3.66 / 10\n\n\n\nEmployment-weighted; unweighted mean: 3.77\n\n\n\n\n\nHigh-vulnerability workers (score ≥ 7)\n\n\n\n2,752,961\n\n\n\n12.1% of total employment\n\n\n\n\n\nSalary-vulnerability index\n\n\n\n~€253,000 M\n\n\n\nEmployment × Salary × Score/10 — an aggregated indicator, not a wage-loss prediction\n\n\n\n\n\nScore range\n\n\n\n1.0 – 9.0\n\n\n\n1.0: hairdressers, cleaners; 9.0: data-entry clerks\n\n\n\n\n\nSalary range\n\n\n\n~13,000 – 79,300 €/year\n\n\n\nReconstructed from EES 2023 + premia + FR/PT proxies\n\n\n\n\n\nSub-component validation (r)\n\n\n\n0.953\n\n\n\nD / C / F / R against FUNCAS expert panel, 7-model adversarial protocol, post-rescoring\n\n\n\n\n\nHolistic validation (κ_w)\n\n\n\n0.667\n\n\n\nAggregated 0–10 score against FUNCAS holistic rating — separate experiment\n\n\n\n\n\nSalary validation (MAPE)\n\n\n\n4.96%\n\n\n\n16 INE EES 2023 reference groups\n\n\n\n\n\nEmployment validation (1-digit)\n\n\n\n±0.00%\n\n\n\nEPA Q4 2025 exact (API Tempus, table 65134)\n\n\n\n\n\nAdversarial incidents\n\n\n\n28 / 24 resolved\n\n\n\nAcross 3 formal red-teaming waves (Appendix G)\n\n\n\n\n\nMethodology\n\nVulnerability scoring\n\nEach occupation receives a vulnerability score on a 0–10 scale, decomposed into four sub-components (the canonical reading per metodologia_v31.pdf):\n\n\n\nD — Digitisability of the core tasks\n\nC — Cognitive complexity that current AI systems can plausibly replicate\n\nF — Physical barrier (need for in-person, embodied execution)\n\nR — Regulatory friction (licences, professional reserves, liability)\n\n\nThe aggregate score reflects partial substitution of tasks, not full job replacement. The exact weighting and the rescore_method are documented in metodologia_v31.pdf (Appendix B) and annotated on the scoring formula shown in the dashboard.\n\nThe methodological lineage follows Brynjolfsson, Mitchell \u0026 Rock (2018) and Eloundou et al. (2024), adapted to the Spanish CNO-11 taxonomy with structural calibration. The score represents a theoretical ceiling under full AI adoption, not a prediction of realised displacement. Empirical evidence (Anthropic Economic Index, March 2026) shows substantial gaps between theoretical vulnerability and observed adoption — only ~21% of Spanish firms currently report AI use (INE-ETICCE 1T2025) — so scores should be read as forward-looking pressure indicators, not horizon-bound forecasts.\n\nFive Spain-specific calibration factors are applied:\n\n\n\nDESI digitalisation index — DESI 2023, 69.8 points. Spain ranks 11th in EU enterprise digital integration. INE-ETICCE 1T2025 reports ~21% of Spanish firms using AI; Banco de España 2025 ~20%. Sector moderation factor: 0.80 (agriculture) to 0.95 (technology/banking).\n\nServices sector weight — 74% of GDP (vs 68% EU average); tourism 12.4% of GDP.\n\nEmployment protection — OECD 3rd-strictest. Unfair-dismissal severance: 33 days/year (max 24 monthly payments). Labour friction factor: 1–5% by sector.\n\nEU AI Act — Regulation (EU) 2024/1689 classifies AI systems by use-case context, not occupations. Annex III high-risk contexts map to a subset of occupations; moderation factor: 2–8% for high-risk categories.\n\nAESIA supervision — Spain is the first EU country with an operational national AI supervisory agency (A Coruña, Real Decreto 729/2023). Fines up to €35 M or 7% of global turnover.\n\n\nEmployment cascade\n\nEPA publishes employment at 1-digit CNO only. To obtain 4-digit estimates, a 4-layer cascade is applied: (1) EPA 1-digit national totals → (2) EPA 2-digit where available → (3) Census 2021 weights at 3-digit → (4) SEPE 2024 contract distributions at 4-digit (with administrative overrides for civil-service corps that bypass SEPE). 4-digit employment figures are estimates, not directly observed data. Result: 499 unique employment values across the 502 occupations.\n\nSalary reconstruction\n\nEncuesta de Estructura Salarial 2023 (INE, table 28186) publishes salaries at 2-digit CNO level (16 reference groups). To disaggregate to 4-digit, the methodology applies INE educational premia (multipliers by required education level) and intra-group structural proxies from France (INSEE) and Portugal (INE-PT) — selected for southern-European labour-market similarity, not for absolute wage levels. Post-correction validation against the 16 INE reference groups: MAPE 4.96%, all deviations under 10%. Three targeted manual corrections were required: Group I (Protection \u0026 security: trienia, danger pay, night-shift supplements), Group M (Fixed machinery operators), Group H (Health \u0026 care).\n\nAdversarial Validation Stack\n\nTwo independent validation experiments (do not conflate)\n\n\n\nSub-component validation — Pearson r = 0.953 between the model-generated D / C / F / R sub-scores and the FUNCAS expert panel ratings, after multi-model rescoring across 7 independent LLMs from different developers.\n\nHolistic validation — Weighted Cohen's κ_w = 0.667 (substantial agreement) between the aggregated 0–10 score and the FUNCAS holistic rating.\n\n\nThe two coefficients come from different targets and different sample designs and are reported separately by design; they are complementary, not directly comparable.\n\nThree adversarial review waves (Appendix G)\n\nThe complete methodology and dataset were subjected to three formal \"destroy this\" red-teaming protocols using multiple models simultaneously, with the explicit instruction to identify methodological flaws, internal inconsistencies, and unsupported claims. Across the three waves: 28 incidents identified, 24 resolved, 4 documented as unresolved residuals. The 4 unresolved incidents are explained in Appendix G with a rationale for non-resolution (data unavailability, source contradiction, or scope boundary).\n\nSalary validation\n\nMAPE 4.96% against 16 INE EES 2023 reference groups (post-correction). All 16 group deviations under 10%. The adversarial protocol caught the Group I deviation (–36.4% pre-correction) and triggered the manual reconciliation that brought it to –4.4%.\n\nEmployment validation\n\nEPA Q4 2025 1-digit totals reproduced with ±0.00% deviation. Maximum difference: 47 persons over the 22.46 M EPA national aggregate, an artefact of the cascade's intra-group rebalancing.\n\nCross-Validation with External Studies — Funcas DT-2026/04 Addendum\n\nThis deposit includes a formal cross-validation note (funcas_validation_addendum.pdf, with companion .md source, raw data CSV, and Python reproducibility script) comparing this dataset with the Funcas Working Paper \"Inteligencia artificial y mercado de trabajo en España\" (Rodríguez-Fernández, April 2026), which applies the AIOE index of Felten et al. (2023) to the CNO-11 taxonomy at the 1-digit level. The addendum reports:\n\n\n\nPearson r = 0.936 between Funcas AIOE-CNO values and the v15 employment-weighted vulnerability aggregated to 9 grand groups\n\nSpearman ρ = 0.830 as a rank-correlation robustness check\n\n4-digit decomposition of the four CNO-11 grand groups flagged by Funcas (groups 1, 2, 3, 4), identifying specific occupations within each group that concentrate vulnerability ≥ 7\n\nDocumented divergence in Group 1 (Directors and managers) where AIOE assigns substantial exposure but no v15 directive occupation reaches the ≥ 7 threshold — interpreted as augmentation rather than substitution\n\n\nThe two methodologies are complementary: Funcas estimates expected displacement under modelled adoption velocity over a 10-year horizon at the 1-digit level; this dataset measures theoretical vulnerability ceiling at the 4-digit level without horizon assumptions. Both readings reinforce the macro ordering of vulnerable occupational groups while offering distinct inputs to public policy.\n\nThe addendum is reproducible from funcas_validation_compute.py running on the v15 JSON: a single Python invocation reconstructs the PDF, Markdown, and CSV bit-for-bit.\n\nLimitations\n\n\n\nVulnerability scores are theoretical estimates, not predictions of job displacement. The Anthropic Economic Index (March 2026) documents significant gaps between theoretical vulnerability (~94% in computer/mathematical occupations) and observed AI adoption (~33%) in the United States, with similar dynamics expected in Spain.\n\n4-digit employment figures are proportional estimates, not observed data. Deviations at 2-digit level against EPA published totals range from ±0% to ±540% due to structural changes between Census 2021 and EPA 2025.\n\nCalibration factors are expert judgement without empirical back-testing. Sensitivity analysis (±20%) shifts the weighted mean vulnerability between approximately 3.0 and 4.5.\n\nFrance / Portugal salary proxies assume structural similarity among southern European economies; not empirically validated at individual occupation level. The MCVL (Muestra Continua de Vidas Laborales) is identified as a future validation source.\n\nScores are generated through a multi-model consensus, but each model performs a single-pass scoring; intra-model reproducibility is estimated at ±0.5 points.\n\nThe analysis is static (March 2026 snapshot) and does not model AI-driven job creation, regional variation, or part-time/full-time distinctions. Self-employed workers (~3.3 M) are excluded from the salary survey by INE design.\n\n\nInteractive Dashboard\n\nThe dashboard at empleo-ai.anlakstudio.com provides four views:\n\n\n\nTreemap — sector-level aggregation with drill-down to individual occupations; rectangle area proportional to employment, colour indicates vulnerability score.\n\nDetailed treemap — occupation-level rectangles nested within sector groups.\n\nScatter plot — salary (y-axis) vs. AI vulnerability (x-axis) with regression trend line; bubble size proportional to employment.\n\nSortable table — tabular view with score, employment, salary, sector, EU AI Act classification, and impact typology.\n\n\nFilters: sector selector, minimum/maximum score range sliders, sort by employment / salary / score.\n\nDetail panel: click any occupation for full profile including the 3–4 sentence Spanish justification, EU AI Act classification, impact typology (Sustitución parcial de tareas / Híbrido / Aumentación), the four D / C / F / R sub-components, and the salary-vulnerability sub-index. The dashboard is bilingual (Spanish / English) via ?lang=en query parameter.\n\nComparative Positioning\n\n\n\n\n\n\nDimension\n\n\n\nThis analysis (v31/v15)\n\n\n\nFuncas DT-2026/04\n\n\n\nOECD AI Exposure\n\n\n\nILO GenAI Index\n\n\n\n\n\n\n\nScope\n\n\n\nSpain\n\n\n\nSpain\n\n\n\nCross-country\n\n\n\nCross-country\n\n\n\n\n\nTaxonomy\n\n\n\nCNO-11 (502 occupations, 4 digits)\n\n\n\nCNO-11 (9 grand groups, 1 digit)\n\n\n\n~400 ISCO\n\n\n\nISCO\n\n\n\n\n\nScoring\n\n\n\nMulti-model + 5 calibration factors + 4-component decomposition (D/C/F/R)\n\n\n\nAIOE (Felten 2023) adapted via SOC→ISCO→CNO\n\n\n\nExpert + O*NET tasks\n\n\n\nGPT-4 task scoring\n\n\n\n\n\nOutput type\n\n\n\nVulnerability ceiling 0–10 (no horizon)\n\n\n\nExpected displacement, 10-year horizon\n\n\n\nExposure score\n\n\n\nExposure score\n\n\n\n\n\nSub-component validation\n\n\n\nr = 0.953 (7 models, vs FUNCAS panel)\n\n\n\nSingle-model (φ = 0.82 attenuation)\n\n\n\nExpert panel\n\n\n\nNone published\n\n\n\n\n\nHolistic validation\n\n\n\nκ_w = 0.667 (vs FUNCAS holistic)\n\n\n\nNot reported\n\n\n\nNot reported\n\n\n\nNot reported\n\n\n\n\n\nAdversarial review\n\n\n\n3 waves, 28 incidents documented\n\n\n\nNone published\n\n\n\nNone published\n\n\n\nNone published\n\n\n\n\n\nRegulatory mapping\n\n\n\nEU AI Act (3 risk levels)\n\n\n\nNone\n\n\n\nNone\n\n\n\nNone\n\n\n\n\n\nSalary cross-reference\n\n\n\nYes (~500 reconstructed values, MAPE 4.96%)\n\n\n\nImplicit (employment-weighted)\n\n\n\nNo\n\n\n\nNo\n\n\n\n\n\nUS Comparative Reference\n\nA parallel reference analysis for the US labour market (Andrej Karpathy, \"Jobs\", 2025–2026) uses BLS / O*NET data on 342 occupations. Key structural differences explain the divergence in headline figures:\n\n\n\n\n\n\nParameter\n\n\n\nUS (Karpathy)\n\n\n\nSpain (this work)\n\n\n\nPrimary cause\n\n\n\n\n\n\n\nMean vulnerability\n\n\n\n~5.3\n\n\n\n3.66 (weighted)\n\n\n\nPhysical-services weight + 5-factor calibration\n\n\n\n\n\n% high vulnerability (≥ 7)\n\n\n\n~42%\n\n\n\n12.1%\n\n\n\nSmaller knowledge-economy share + employment-protection friction\n\n\n\n\n\nRegulatory classification\n\n\n\nNot included\n\n\n\nEU AI Act 3-tier mapping\n\n\n\nNo US federal AI framework\n\n\n\n\n\nSalary granularity\n\n\n\n~800 direct BLS values\n\n\n\n~500 reconstructed values\n\n\n\nINE publishes EES at 2-digit level\n\n\n\n\n\nEmployment granularity\n\n\n\nDirect per occupation\n\n\n\nDistributed from 1-digit\n\n\n\nEPA anonymises CNO at 1-digit\n\n\n\n\n\nOECD contextualisation: OECD's 28% \"at risk\" figure (Employment Outlook 2024) refers to all automation technologies, not exclusively AI. OECD AI-specific figures for Spain: 5.9% high automation risk from AI; 27.4% GenAI exposure. This analysis's 12.1% (score ≥ 7) measures calibrated theoretical vulnerability to AI broadly — not directly comparable to any single OECD figure.\n\nTechnical Notes\n\nThe methodology document (v31) contains 57 technical notes organised across 7 appendices (A–G), covering: complete technical notes by topic, sub-component decomposition (D / C / F / R) for the 502 occupations, salary cascade with the full 16-group MAPE table and three targeted corrections, EU AI Act mapping protocol (Annex III contexts to occupations), sector taxonomy and the 12-category assignment logic, sensitivity analysis (±20% on each calibration factor), and the adversarial review log (Appendix G).\n\nSelected technical notes referenced in this description:\n\n\n\nNotes on employment cascade: EPA publishes CNO at 1-digit only; 4-digit figures are Census 2021- and SEPE 2024-weighted proportional estimates.\n\nNotes on salary methodology: EES 2023 reference year is 2022, with no temporal deflator applied. Self-employed workers excluded by INE design. France (INSEE) and Portugal (INE-PT) proxies selected for southern-European structural similarity.\n\nNotes on scoring protocol ([13]–[15]): Multi-model consensus protocol with intra-model reproducibility ±0.5 points; few-shot calibration anchors at scores 1, 5, and 9; rescore_method annotated on the scoring formula.\n\nNote on regulatory mapping: Art. 5 of the EU AI Act prohibits certain AI practices, not professions; no \"prohibited\" category at occupation level.\n\nNote [32] (theory–practice gap): The Anthropic Economic Index (March 2026) suggests calibration factors may understate the full adoption gap between theoretical vulnerability and observed AI use.\n\n\nFiles in This Deposit\n\nDataset (v15)\n\n\n\nspain_502_v15_subcomp_complete.json — full dataset, 502 occupations, all fields including new cumulative analysis fields and the four D / C / F / R sub-components\n\nspain_v15_threshold_lookup.csv — inverse threshold mapping (workforce share at or above each score)\n\n\nMethodology (v31)\n\n\n\nmetodologia_v31.pdf — methodology document (~46 pages, Spanish) including the 7 appendices (A–G) with the adversarial review log and rescore_method annotation\n\n\nFuncas Cross-Validation Addendum (v1)\n\n\n\nfuncas_validation_addendum.pdf — 12-page methodological note\n\nfuncas_validation_addendum.md — Markdown source\n\nfuncas_validation_data.csv — raw cross-validation table (10 grand groups × 12 fields)\n\nfuncas_validation_compute.py — Python reproducibility script\n\n\nSee the file panel on this Zenodo record for the complete list and download links.\n\nCitation\n\nDe Nicolás, Á. (2026). AI Vulnerability of Jobs in Spain — Complete Dataset, Methodology, Funcas Cross-Validation Addendum \u0026 Interactive Dashboard (Methodology v31 / Dataset v15 + Funcas Addendum v1) [Data set]. Anlak Studio. Zenodo. [New DOI assigned upon publication of this version]\n\nConcept DOI (resolves to latest version): https://doi.org/10.5281/zenodo.19076797 Previous version (v30 / v15, May 2026): https://doi.org/10.5281/zenodo.20031741\n\nKeywords\n\nartificial intelligence; AI vulnerability; labour market; employment; Spain; CNO-11; EPA; EU AI Act; AESIA; automation; occupational risk; salary estimation; multi-model validation; adversarial review; red teaming; inter-model agreement; treemap; salary-vulnerability index; cross-validation; Funcas; Rodríguez-Fernández; Felten et al.; AIOE; Eloundou et al.; Acemoglu\n\nLicense\n\nCreative Commons Attribution 4.0 International (CC BY 4.0). The dataset, methodology, dashboard source code, and all derived materials may be reused and adapted with attribution.\n\nLanguage\n\nSpanish (dataset, justifications, dashboard primary UI); English (this description, dashboard via ?lang=en, abstract and methodology summary).\n\nResource Type\n\nDataset + Interactive Visualisation + Methodology Document\n\nRelated Identifiers\n\n\n\nempleo-ai.anlakstudio.com — interactive dashboard (IsSupplementedBy)\n\n10.5281/zenodo.19076797 — concept DOI for all versions and methodological notes (IsVersionOf)\n\n10.5281/zenodo.20031741 — previous version, methodology v30 / dataset v15 (IsNewVersionOf)\n\nRodríguez-Fernández, F. (2026). Inteligencia artificial y mercado de trabajo en España. Exposición ocupacional, efectos sobre el empleo y adopción empresarial. Funcas Working Paper DT-2026/04. (IsRelatedTo — cross-validated in the bundled addendum)\n\nBrynjolfsson, E., Mitchell, T. \u0026 Rock, D. (2018). What Can Machines Learn, and What Does It Mean for Occupations and the Economy? AEA Papers and Proceedings, 108: 43–47. (References)\n\nEloundou, T., Manning, S., Mishkin, P. \u0026 Rock, D. (2024). GPTs are GPTs: Labor market impact potential of LLMs. Science, 384(6702): 1306–1308. https://doi.org/10.1126/science.adj0998 (References)\n\nFelten, E. W., Raj, M. \u0026 Seamans, R. (2023). Occupational Heterogeneity in Exposure to Generative AI. SSRN Working Paper 4414065. (References)\n\nAcemoglu, D. (2024). The Simple Macroeconomics of AI. NBER Working Paper 32487; Economic Policy 40(121): 13–58. https://doi.org/10.3386/w32487 (References)\n\nFrey, C. B. \u0026 Osborne, M. A. (2017). The Future of Employment. Technological Forecasting and Social Change, 114: 254–280. (References)\n\nRegulation (EU) 2024/1689 (EU AI Act). Annex III, Arts. 5 and 6. (References)\n\nAnthropic. The Anthropic Economic Index, March 2026. (References)\n\nNedelkoska, L. \u0026 Quintini, G. (2018). Automation, skills use and training. OECD Social,\n\n\n ","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19076797","contentUrl":null,"metadataVersion":22,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":13,"versionOfCount":1,"created":"2026-03-18T00:16:25Z","registered":"2026-03-18T00:16:25Z","published":null,"updated":"2026-05-09T17:38:33Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.20031741","type":"dois","attributes":{"doi":"10.5281/zenodo.20031741","identifiers":[{"identifier":"oai:zenodo.org:20031741","identifierType":"oai"},{"identifier":"DOI 10.5281/zenodo.19076797","identifierType":"Handle"}],"creators":[{"name":"de Nicolas, Alvaro","nameType":"Personal","givenName":"Alvaro","familyName":"de Nicolas","nameIdentifiers":[{"nameIdentifier":"0009-0004-8234-9720","nameIdentifierScheme":"ORCID"}],"affiliation":[]},{"name":"Sureda, Miguel","nameType":"Personal","givenName":"Miguel","familyName":"Sureda","affiliation":["Anlak Studio"],"nameIdentifiers":[]}],"titles":[{"title":"Vulnerabilidad de Empleos a la Inteligencia Artificial en España: Dataset, Metodología y Dashboard Interactivo (v30 / v15 + Funcas Cross-Validation Addendum)"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Artificial intelligence","subjectScheme":"EuroSciVoc"},{"subject":"Labour market","subjectScheme":"GEMET"},{"subject":"Employment","subjectScheme":"EuroSciVoc"},{"subject":"Spain"},{"subject":"occupational risk"},{"subject":"EU AI Act"},{"subject":"CNO-11"},{"subject":"reproducibility"},{"subject":"EPA"},{"subject":"automation"},{"subject":"interactive dashboard"},{"subject":"inter-model validation"},{"subject":"Bland-Altman"},{"subject":"treemap"},{"subject":"AESIA"},{"subject":"sub-component scoring"},{"subject":"unemployment"},{"subject":"SEPE"},{"subject":"adoption gap"},{"subject":"adversarial audit"},{"subject":"red team"},{"subject":"FUNCAS"},{"subject":"AI vulnerability"},{"subject":"salary-vulnerability index"},{"subject":"Rodríguez-Fernández"},{"subject":"Felten et al."},{"subject":"AIOE"},{"subject":"cross-validation"}],"contributors":[],"dates":[{"date":"2026-05-05","dateType":"Issued"},{"date":"2026-05-05","dateType":"Updated"}],"language":"es","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementedBy","relatedIdentifier":"http://empleo-ai.anlakstudio.com/","resourceTypeGeneral":"Dataset","relatedIdentifierType":"URL"},{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19076797","resourceTypeGeneral":"Text","relatedIdentifierType":"DOI"},{"relationType":"IsNewVersionOf","relatedIdentifier":"10.5281/zenodo.19186444","resourceTypeGeneral":"Dataset","relatedIdentifierType":"DOI"},{"relationType":"References","relatedIdentifier":"https://www.funcas.es/documentos_trabajo/inteligencia-artificial-y-mercado-de-trabajo-en-espana-exposicion-ocupacional-efectos-sobre-el-empleo-y-adopcion-empresarial/","resourceTypeGeneral":"Text","relatedIdentifierType":"URL"},{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19076797","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"v30 (methodology) / v15 (dataset) + Funcas addendum v1","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Vulnerabilidad de Empleos a la Inteligencia Artificial en España: Dataset, Metodología y Dashboard Interactivo (v31 / v15 + Funcas Cross-Validation Addendum)\n\nAI Vulnerability of Jobs in Spain — Complete Dataset, Methodology, Funcas Cross-Validation Addendum \u0026 Interactive Dashboard\n\nThis deposit contains the complete dataset, methodology, a methodological cross-validation addendum against the Funcas Working Paper DT-2026/04 (Rodríguez-Fernández, April 2026), and the interactive visualisation tool for assessing the theoretical vulnerability of 502 Spanish occupations to artificial intelligence. The analysis covers 22,732,223 workers (EPA Q4 2025, INE) and assigns each occupation a calibrated vulnerability score on a 0–10 scale, cross-referenced with salary data, EU AI Act risk classification, and impact typology.\n\nThe interactive dashboard is publicly available at: https://empleo-ai.anlakstudio.com\n\nThis is methodology v31 / dataset v15, deposited May 2026, and includes the Funcas Cross-Validation Addendum (v1) documenting a formal validation against Funcas Working Paper DT-2026/04 (Rodríguez-Fernández, April 2026) with Pearson r = 0.936 across the 9 grand groups CNO-11. The deposit is part of the project series under concept DOI 10.5281/zenodo.19076797, which collects all versions and complementary methodological notes.\n\nWhat's New in This Version\n\n\n\n\n\n\nComponent\n\n\n\nChange\n\n\n\n\n\nv30/v15 → v31/v15\n\n\n\nMethodology v31: terminology of the four sub-components aligned with the PDF (Digitisability D / Cognitive complexity C / Physical barrier F / Regulatory friction R); validation reporting separated into two distinct experiments (sub-component vs. holistic); description text reconciled with metodologia_v31.pdf\n\n\n\n\n\nSub-component validation\n\n\n\nPearson r = 0.953 on the four D / C / F / R sub-scores against the FUNCAS expert panel (post-rescoring, 7 models)\n\n\n\n\n\nHolistic validation\n\n\n\nWeighted Cohen's κ_w = 0.667 (substantial agreement) on the aggregated 0–10 score against the FUNCAS holistic rating — a separate experiment, not directly comparable to the sub-component r\n\n\n\n\n\nCross-validation\n\n\n\nFuncas Addendum v1 (unchanged): formal cross-validation against Funcas DT-2026/04 (Rodríguez-Fernández, April 2026) — Pearson r = 0.936, Spearman ρ = 0.830 across 9 grand groups CNO-11\n\n\n\n\n\nDecomposition\n\n\n\n4-digit decomposition of the four CNO-11 groups flagged by Funcas (groups 1, 2, 3, 4) — included in the addendum PDF and dataset\n\n\n\n\n\nMethodology\n\n\n\nv31 — 46+ pages with 57 technical notes and 7 appendices (A–G), including adversarial review log and the explicit rescore_method annotation on the scoring formula\n\n\n\n\n\nAdversarial review\n\n\n\nFormal red-teaming protocol (Appendix G): 3 waves, 28 incidents identified, 24 resolved\n\n\n\n\n\nSalary cascade\n\n\n\nValidated against 16 EES 2023 reference groups, MAPE 4.96% (post-correction)\n\n\n\n\n\nTotal workforce\n\n\n\n4-layer cascade: 22,732,223 (EPA → Census 2021 → SEPE 2024)\n\n\n\n\n\nHigh-vulnerability cohort\n\n\n\n2,752,961 workers (12.1%) at score ≥ 7 across the 502 occupations\n\n\n\n\n\nSalary-vulnerability index\n\n\n\n~€253,000 M (employment × salary × score/10, an aggregated indicator, not a wage-loss prediction)\n\n\n\n\n\nDataset\n\nThe core dataset (spain_502_v15_subcomp_complete.json) contains 502 records corresponding to the complete CNO-11 occupational taxonomy (SEPE expansion). Each record includes the following fields:\n\n\n\n\n\n\nField\n\n\n\nType\n\n\n\nDescription\n\n\n\n\n\n\n\ncno\n\n\n\nstring\n\n\n\n4-digit CNO-11 occupation code\n\n\n\n\n\nnombre\n\n\n\nstring\n\n\n\nOfficial occupation name (Spanish)\n\n\n\n\n\nsector\n\n\n\nstring\n\n\n\nAssigned economic sector (12 categories)\n\n\n\n\n\nempleo\n\n\n\ninteger\n\n\n\nEstimated employment (EPA Q4 2025, redistributed via Census 2021 + SEPE 2024 weights)\n\n\n\n\n\nsalario_medio_eur\n\n\n\nfloat\n\n\n\nEstimated mean gross annual salary (EUR), based on INE EES 2023 + educational premia + FR/PT proxies\n\n\n\n\n\nvulnerabilidad_ia_score\n\n\n\nfloat\n\n\n\nAI vulnerability score (0–10), calibrated for Spain; 4-component decomposition (D / C / F / R)\n\n\n\n\n\neu_ai_act\n\n\n\nstring\n\n\n\nEU AI Act risk classification (\"Alto riesgo\" Annex III, \"Riesgo limitado\", \"Riesgo mínimo\")\n\n\n\n\n\ntipo_impacto\n\n\n\nstring\n\n\n\nImpact typology: \"Sustitución parcial de tareas\", \"Híbrido\", or \"Aumentación\"\n\n\n\n\n\njustificacion\n\n\n\nstring\n\n\n\n3–4 sentence justification in Spanish explaining the automation vector and human-protective factors\n\n\n\n\n\ncensus_2021_employed\n\n\n\nfloat\n\n\n\nCensus 2021 employment figure used for intra-group weighting\n\n\n\n\n\nemployment_method\n\n\n\nstring\n\n\n\nEmployment estimation method identifier\n\n\n\n\n\nD_digitalizabilidad\n\n\n\nfloat\n\n\n\nSub-component D — Digitisability of core tasks\n\n\n\n\n\nC_complejidad_cognitiva\n\n\n\nfloat\n\n\n\nSub-component C — Cognitive complexity AI can plausibly replicate\n\n\n\n\n\nF_barrera_fisica\n\n\n\nfloat\n\n\n\nSub-component F — Physical barrier (need for in-person, embodied execution)\n\n\n\n\n\nR_friccion_regulatoria\n\n\n\nfloat\n\n\n\nSub-component R — Regulatory friction (licences, professional reserves, liability)\n\n\n\n\n\nrank_in_descending\n\n\n\ninteger (v15)\n\n\n\nRank position when the 502 occupations are sorted by descending vulnerability score\n\n\n\n\n\ncumulative_workers_descending\n\n\n\ninteger (v15)\n\n\n\nRunning total of workers when occupations are summed in descending-score order\n\n\n\n\n\npct_workforce_descending\n\n\n\nfloat (v15)\n\n\n\nCumulative percentage of total workforce at or above this rank\n\n\n\n\n\ncumulative_workers_at_or_above_score\n\n\n\ninteger (v15)\n\n\n\nTotal workers in occupations with score ≥ this occupation's score\n\n\n\n\n\npct_workforce_at_or_above_score\n\n\n\nfloat (v15)\n\n\n\nPercentage of total workforce in occupations at or above this score\n\n\n\n\n\nThe dataset distinguishes 499 unique employment values across the 502 occupations, providing the most disaggregated employment-by-occupation estimation available for Spain at the 4-digit CNO-11 level. The cumulative fields in v15 allow direct inverse-threshold queries (e.g., \"what fraction of the Spanish workforce is in occupations with score ≥ 7.5?\") without re-aggregation, and are also published as a standalone CSV (spain_v15_threshold_lookup.csv) for spreadsheet use.\n\nKey Findings\n\n\n\n\n\n\nIndicator\n\n\n\nValue\n\n\n\nNote\n\n\n\n\n\n\n\nOccupations analysed\n\n\n\n502\n\n\n\nComplete CNO-11 (SEPE taxonomy)\n\n\n\n\n\nWorkers represented\n\n\n\n22,732,223\n\n\n\nEPA Q4 2025 (final data) reassigned to 4-digit CNO-11\n\n\n\n\n\nWeighted mean vulnerability\n\n\n\n3.66 / 10\n\n\n\nEmployment-weighted; unweighted mean: 3.77\n\n\n\n\n\nHigh-vulnerability workers (score ≥ 7)\n\n\n\n2,752,961\n\n\n\n12.1% of total employment\n\n\n\n\n\nSalary-vulnerability index\n\n\n\n~€253,000 M\n\n\n\nEmployment × Salary × Score/10 — an aggregated indicator, not a wage-loss prediction\n\n\n\n\n\nScore range\n\n\n\n1.0 – 9.0\n\n\n\n1.0: hairdressers, cleaners; 9.0: data-entry clerks\n\n\n\n\n\nSalary range\n\n\n\n~13,000 – 79,300 €/year\n\n\n\nReconstructed from EES 2023 + premia + FR/PT proxies\n\n\n\n\n\nSub-component validation (r)\n\n\n\n0.953\n\n\n\nD / C / F / R against FUNCAS expert panel, 7-model adversarial protocol, post-rescoring\n\n\n\n\n\nHolistic validation (κ_w)\n\n\n\n0.667\n\n\n\nAggregated 0–10 score against FUNCAS holistic rating — separate experiment\n\n\n\n\n\nSalary validation (MAPE)\n\n\n\n4.96%\n\n\n\n16 INE EES 2023 reference groups\n\n\n\n\n\nEmployment validation (1-digit)\n\n\n\n±0.00%\n\n\n\nEPA Q4 2025 exact (API Tempus, table 65134)\n\n\n\n\n\nAdversarial incidents\n\n\n\n28 / 24 resolved\n\n\n\nAcross 3 formal red-teaming waves (Appendix G)\n\n\n\n\n\nMethodology\n\nVulnerability scoring\n\nEach occupation receives a vulnerability score on a 0–10 scale, decomposed into four sub-components (the canonical reading per metodologia_v31.pdf):\n\n\n\nD — Digitisability of the core tasks\n\nC — Cognitive complexity that current AI systems can plausibly replicate\n\nF — Physical barrier (need for in-person, embodied execution)\n\nR — Regulatory friction (licences, professional reserves, liability)\n\n\nThe aggregate score reflects partial substitution of tasks, not full job replacement. The exact weighting and the rescore_method are documented in metodologia_v31.pdf (Appendix B) and annotated on the scoring formula shown in the dashboard.\n\nThe methodological lineage follows Brynjolfsson, Mitchell \u0026 Rock (2018) and Eloundou et al. (2024), adapted to the Spanish CNO-11 taxonomy with structural calibration. The score represents a theoretical ceiling under full AI adoption, not a prediction of realised displacement. Empirical evidence (Anthropic Economic Index, March 2026) shows substantial gaps between theoretical vulnerability and observed adoption — only ~21% of Spanish firms currently report AI use (INE-ETICCE 1T2025) — so scores should be read as forward-looking pressure indicators, not horizon-bound forecasts.\n\nFive Spain-specific calibration factors are applied:\n\n\n\nDESI digitalisation index — DESI 2023, 69.8 points. Spain ranks 11th in EU enterprise digital integration. INE-ETICCE 1T2025 reports ~21% of Spanish firms using AI; Banco de España 2025 ~20%. Sector moderation factor: 0.80 (agriculture) to 0.95 (technology/banking).\n\nServices sector weight — 74% of GDP (vs 68% EU average); tourism 12.4% of GDP.\n\nEmployment protection — OECD 3rd-strictest. Unfair-dismissal severance: 33 days/year (max 24 monthly payments). Labour friction factor: 1–5% by sector.\n\nEU AI Act — Regulation (EU) 2024/1689 classifies AI systems by use-case context, not occupations. Annex III high-risk contexts map to a subset of occupations; moderation factor: 2–8% for high-risk categories.\n\nAESIA supervision — Spain is the first EU country with an operational national AI supervisory agency (A Coruña, Real Decreto 729/2023). Fines up to €35 M or 7% of global turnover.\n\n\nEmployment cascade\n\nEPA publishes employment at 1-digit CNO only. To obtain 4-digit estimates, a 4-layer cascade is applied: (1) EPA 1-digit national totals → (2) EPA 2-digit where available → (3) Census 2021 weights at 3-digit → (4) SEPE 2024 contract distributions at 4-digit (with administrative overrides for civil-service corps that bypass SEPE). 4-digit employment figures are estimates, not directly observed data. Result: 499 unique employment values across the 502 occupations.\n\nSalary reconstruction\n\nEncuesta de Estructura Salarial 2023 (INE, table 28186) publishes salaries at 2-digit CNO level (16 reference groups). To disaggregate to 4-digit, the methodology applies INE educational premia (multipliers by required education level) and intra-group structural proxies from France (INSEE) and Portugal (INE-PT) — selected for southern-European labour-market similarity, not for absolute wage levels. Post-correction validation against the 16 INE reference groups: MAPE 4.96%, all deviations under 10%. Three targeted manual corrections were required: Group I (Protection \u0026 security: trienia, danger pay, night-shift supplements), Group M (Fixed machinery operators), Group H (Health \u0026 care).\n\nAdversarial Validation Stack\n\nTwo independent validation experiments (do not conflate)\n\n\n\nSub-component validation — Pearson r = 0.953 between the model-generated D / C / F / R sub-scores and the FUNCAS expert panel ratings, after multi-model rescoring across 7 independent LLMs from different developers.\n\nHolistic validation — Weighted Cohen's κ_w = 0.667 (substantial agreement) between the aggregated 0–10 score and the FUNCAS holistic rating.\n\n\nThe two coefficients come from different targets and different sample designs and are reported separately by design; they are complementary, not directly comparable.\n\nThree adversarial review waves (Appendix G)\n\nThe complete methodology and dataset were subjected to three formal \"destroy this\" red-teaming protocols using multiple models simultaneously, with the explicit instruction to identify methodological flaws, internal inconsistencies, and unsupported claims. Across the three waves: 28 incidents identified, 24 resolved, 4 documented as unresolved residuals. The 4 unresolved incidents are explained in Appendix G with a rationale for non-resolution (data unavailability, source contradiction, or scope boundary).\n\nSalary validation\n\nMAPE 4.96% against 16 INE EES 2023 reference groups (post-correction). All 16 group deviations under 10%. The adversarial protocol caught the Group I deviation (–36.4% pre-correction) and triggered the manual reconciliation that brought it to –4.4%.\n\nEmployment validation\n\nEPA Q4 2025 1-digit totals reproduced with ±0.00% deviation. Maximum difference: 47 persons over the 22.46 M EPA national aggregate, an artefact of the cascade's intra-group rebalancing.\n\nCross-Validation with External Studies — Funcas DT-2026/04 Addendum\n\nThis deposit includes a formal cross-validation note (funcas_validation_addendum.pdf, with companion .md source, raw data CSV, and Python reproducibility script) comparing this dataset with the Funcas Working Paper \"Inteligencia artificial y mercado de trabajo en España\" (Rodríguez-Fernández, April 2026), which applies the AIOE index of Felten et al. (2023) to the CNO-11 taxonomy at the 1-digit level. The addendum reports:\n\n\n\nPearson r = 0.936 between Funcas AIOE-CNO values and the v15 employment-weighted vulnerability aggregated to 9 grand groups\n\nSpearman ρ = 0.830 as a rank-correlation robustness check\n\n4-digit decomposition of the four CNO-11 grand groups flagged by Funcas (groups 1, 2, 3, 4), identifying specific occupations within each group that concentrate vulnerability ≥ 7\n\nDocumented divergence in Group 1 (Directors and managers) where AIOE assigns substantial exposure but no v15 directive occupation reaches the ≥ 7 threshold — interpreted as augmentation rather than substitution\n\n\nThe two methodologies are complementary: Funcas estimates expected displacement under modelled adoption velocity over a 10-year horizon at the 1-digit level; this dataset measures theoretical vulnerability ceiling at the 4-digit level without horizon assumptions. Both readings reinforce the macro ordering of vulnerable occupational groups while offering distinct inputs to public policy.\n\nThe addendum is reproducible from funcas_validation_compute.py running on the v15 JSON: a single Python invocation reconstructs the PDF, Markdown, and CSV bit-for-bit.\n\nLimitations\n\n\n\nVulnerability scores are theoretical estimates, not predictions of job displacement. The Anthropic Economic Index (March 2026) documents significant gaps between theoretical vulnerability (~94% in computer/mathematical occupations) and observed AI adoption (~33%) in the United States, with similar dynamics expected in Spain.\n\n4-digit employment figures are proportional estimates, not observed data. Deviations at 2-digit level against EPA published totals range from ±0% to ±540% due to structural changes between Census 2021 and EPA 2025.\n\nCalibration factors are expert judgement without empirical back-testing. Sensitivity analysis (±20%) shifts the weighted mean vulnerability between approximately 3.0 and 4.5.\n\nFrance / Portugal salary proxies assume structural similarity among southern European economies; not empirically validated at individual occupation level. The MCVL (Muestra Continua de Vidas Laborales) is identified as a future validation source.\n\nScores are generated through a multi-model consensus, but each model performs a single-pass scoring; intra-model reproducibility is estimated at ±0.5 points.\n\nThe analysis is static (March 2026 snapshot) and does not model AI-driven job creation, regional variation, or part-time/full-time distinctions. Self-employed workers (~3.3 M) are excluded from the salary survey by INE design.\n\n\nInteractive Dashboard\n\nThe dashboard at empleo-ai.anlakstudio.com provides four views:\n\n\n\nTreemap — sector-level aggregation with drill-down to individual occupations; rectangle area proportional to employment, colour indicates vulnerability score.\n\nDetailed treemap — occupation-level rectangles nested within sector groups.\n\nScatter plot — salary (y-axis) vs. AI vulnerability (x-axis) with regression trend line; bubble size proportional to employment.\n\nSortable table — tabular view with score, employment, salary, sector, EU AI Act classification, and impact typology.\n\n\nFilters: sector selector, minimum/maximum score range sliders, sort by employment / salary / score.\n\nDetail panel: click any occupation for full profile including the 3–4 sentence Spanish justification, EU AI Act classification, impact typology (Sustitución parcial de tareas / Híbrido / Aumentación), the four D / C / F / R sub-components, and the salary-vulnerability sub-index. The dashboard is bilingual (Spanish / English) via ?lang=en query parameter.\n\nComparative Positioning\n\n\n\n\n\n\nDimension\n\n\n\nThis analysis (v31/v15)\n\n\n\nFuncas DT-2026/04\n\n\n\nOECD AI Exposure\n\n\n\nILO GenAI Index\n\n\n\n\n\n\n\nScope\n\n\n\nSpain\n\n\n\nSpain\n\n\n\nCross-country\n\n\n\nCross-country\n\n\n\n\n\nTaxonomy\n\n\n\nCNO-11 (502 occupations, 4 digits)\n\n\n\nCNO-11 (9 grand groups, 1 digit)\n\n\n\n~400 ISCO\n\n\n\nISCO\n\n\n\n\n\nScoring\n\n\n\nMulti-model + 5 calibration factors + 4-component decomposition (D/C/F/R)\n\n\n\nAIOE (Felten 2023) adapted via SOC→ISCO→CNO\n\n\n\nExpert + O*NET tasks\n\n\n\nGPT-4 task scoring\n\n\n\n\n\nOutput type\n\n\n\nVulnerability ceiling 0–10 (no horizon)\n\n\n\nExpected displacement, 10-year horizon\n\n\n\nExposure score\n\n\n\nExposure score\n\n\n\n\n\nSub-component validation\n\n\n\nr = 0.953 (7 models, vs FUNCAS panel)\n\n\n\nSingle-model (φ = 0.82 attenuation)\n\n\n\nExpert panel\n\n\n\nNone published\n\n\n\n\n\nHolistic validation\n\n\n\nκ_w = 0.667 (vs FUNCAS holistic)\n\n\n\nNot reported\n\n\n\nNot reported\n\n\n\nNot reported\n\n\n\n\n\nAdversarial review\n\n\n\n3 waves, 28 incidents documented\n\n\n\nNone published\n\n\n\nNone published\n\n\n\nNone published\n\n\n\n\n\nRegulatory mapping\n\n\n\nEU AI Act (3 risk levels)\n\n\n\nNone\n\n\n\nNone\n\n\n\nNone\n\n\n\n\n\nSalary cross-reference\n\n\n\nYes (~500 reconstructed values, MAPE 4.96%)\n\n\n\nImplicit (employment-weighted)\n\n\n\nNo\n\n\n\nNo\n\n\n\n\n\nUS Comparative Reference\n\nA parallel reference analysis for the US labour market (Andrej Karpathy, \"Jobs\", 2025–2026) uses BLS / O*NET data on 342 occupations. Key structural differences explain the divergence in headline figures:\n\n\n\n\n\n\nParameter\n\n\n\nUS (Karpathy)\n\n\n\nSpain (this work)\n\n\n\nPrimary cause\n\n\n\n\n\n\n\nMean vulnerability\n\n\n\n~5.3\n\n\n\n3.66 (weighted)\n\n\n\nPhysical-services weight + 5-factor calibration\n\n\n\n\n\n% high vulnerability (≥ 7)\n\n\n\n~42%\n\n\n\n12.1%\n\n\n\nSmaller knowledge-economy share + employment-protection friction\n\n\n\n\n\nRegulatory classification\n\n\n\nNot included\n\n\n\nEU AI Act 3-tier mapping\n\n\n\nNo US federal AI framework\n\n\n\n\n\nSalary granularity\n\n\n\n~800 direct BLS values\n\n\n\n~500 reconstructed values\n\n\n\nINE publishes EES at 2-digit level\n\n\n\n\n\nEmployment granularity\n\n\n\nDirect per occupation\n\n\n\nDistributed from 1-digit\n\n\n\nEPA anonymises CNO at 1-digit\n\n\n\n\n\nOECD contextualisation: OECD's 28% \"at risk\" figure (Employment Outlook 2024) refers to all automation technologies, not exclusively AI. OECD AI-specific figures for Spain: 5.9% high automation risk from AI; 27.4% GenAI exposure. This analysis's 12.1% (score ≥ 7) measures calibrated theoretical vulnerability to AI broadly — not directly comparable to any single OECD figure.\n\nTechnical Notes\n\nThe methodology document (v31) contains 57 technical notes organised across 7 appendices (A–G), covering: complete technical notes by topic, sub-component decomposition (D / C / F / R) for the 502 occupations, salary cascade with the full 16-group MAPE table and three targeted corrections, EU AI Act mapping protocol (Annex III contexts to occupations), sector taxonomy and the 12-category assignment logic, sensitivity analysis (±20% on each calibration factor), and the adversarial review log (Appendix G).\n\nSelected technical notes referenced in this description:\n\n\n\nNotes on employment cascade: EPA publishes CNO at 1-digit only; 4-digit figures are Census 2021- and SEPE 2024-weighted proportional estimates.\n\nNotes on salary methodology: EES 2023 reference year is 2022, with no temporal deflator applied. Self-employed workers excluded by INE design. France (INSEE) and Portugal (INE-PT) proxies selected for southern-European structural similarity.\n\nNotes on scoring protocol ([13]–[15]): Multi-model consensus protocol with intra-model reproducibility ±0.5 points; few-shot calibration anchors at scores 1, 5, and 9; rescore_method annotated on the scoring formula.\n\nNote on regulatory mapping: Art. 5 of the EU AI Act prohibits certain AI practices, not professions; no \"prohibited\" category at occupation level.\n\nNote [32] (theory–practice gap): The Anthropic Economic Index (March 2026) suggests calibration factors may understate the full adoption gap between theoretical vulnerability and observed AI use.\n\n\nFiles in This Deposit\n\nDataset (v15)\n\n\n\nspain_502_v15_subcomp_complete.json — full dataset, 502 occupations, all fields including new cumulative analysis fields and the four D / C / F / R sub-components\n\nspain_v15_threshold_lookup.csv — inverse threshold mapping (workforce share at or above each score)\n\n\nMethodology (v31)\n\n\n\nmetodologia_v31.pdf — methodology document (~46 pages, Spanish) including the 7 appendices (A–G) with the adversarial review log and rescore_method annotation\n\n\nFuncas Cross-Validation Addendum (v1)\n\n\n\nfuncas_validation_addendum.pdf — 12-page methodological note\n\nfuncas_validation_addendum.md — Markdown source\n\nfuncas_validation_data.csv — raw cross-validation table (10 grand groups × 12 fields)\n\nfuncas_validation_compute.py — Python reproducibility script\n\n\nSee the file panel on this Zenodo record for the complete list and download links.\n\nCitation\n\nDe Nicolás, Á. (2026). AI Vulnerability of Jobs in Spain — Complete Dataset, Methodology, Funcas Cross-Validation Addendum \u0026 Interactive Dashboard (Methodology v31 / Dataset v15 + Funcas Addendum v1) [Data set]. Anlak Studio. Zenodo. [New DOI assigned upon publication of this version]\n\nConcept DOI (resolves to latest version): https://doi.org/10.5281/zenodo.19076797 Previous version (v30 / v15, May 2026): https://doi.org/10.5281/zenodo.20031741\n\nKeywords\n\nartificial intelligence; AI vulnerability; labour market; employment; Spain; CNO-11; EPA; EU AI Act; AESIA; automation; occupational risk; salary estimation; multi-model validation; adversarial review; red teaming; inter-model agreement; treemap; salary-vulnerability index; cross-validation; Funcas; Rodríguez-Fernández; Felten et al.; AIOE; Eloundou et al.; Acemoglu\n\nLicense\n\nCreative Commons Attribution 4.0 International (CC BY 4.0). The dataset, methodology, dashboard source code, and all derived materials may be reused and adapted with attribution.\n\nLanguage\n\nSpanish (dataset, justifications, dashboard primary UI); English (this description, dashboard via ?lang=en, abstract and methodology summary).\n\nResource Type\n\nDataset + Interactive Visualisation + Methodology Document\n\nRelated Identifiers\n\n\n\nempleo-ai.anlakstudio.com — interactive dashboard (IsSupplementedBy)\n\n10.5281/zenodo.19076797 — concept DOI for all versions and methodological notes (IsVersionOf)\n\n10.5281/zenodo.20031741 — previous version, methodology v30 / dataset v15 (IsNewVersionOf)\n\nRodríguez-Fernández, F. (2026). Inteligencia artificial y mercado de trabajo en España. Exposición ocupacional, efectos sobre el empleo y adopción empresarial. Funcas Working Paper DT-2026/04. (IsRelatedTo — cross-validated in the bundled addendum)\n\nBrynjolfsson, E., Mitchell, T. \u0026 Rock, D. (2018). What Can Machines Learn, and What Does It Mean for Occupations and the Economy? AEA Papers and Proceedings, 108: 43–47. (References)\n\nEloundou, T., Manning, S., Mishkin, P. \u0026 Rock, D. (2024). GPTs are GPTs: Labor market impact potential of LLMs. Science, 384(6702): 1306–1308. https://doi.org/10.1126/science.adj0998 (References)\n\nFelten, E. W., Raj, M. \u0026 Seamans, R. (2023). Occupational Heterogeneity in Exposure to Generative AI. SSRN Working Paper 4414065. (References)\n\nAcemoglu, D. (2024). The Simple Macroeconomics of AI. NBER Working Paper 32487; Economic Policy 40(121): 13–58. https://doi.org/10.3386/w32487 (References)\n\nFrey, C. B. \u0026 Osborne, M. A. (2017). The Future of Employment. Technological Forecasting and Social Change, 114: 254–280. (References)\n\nRegulation (EU) 2024/1689 (EU AI Act). Annex III, Arts. 5 and 6. (References)\n\nAnthropic. The Anthropic Economic Index, March 2026. (References)\n\nNedelkoska, L. \u0026 Quintini, G. (2018). Automation, skills use and training. OECD Social,\n\n\n ","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.20031741","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-05-05T01:33:57Z","registered":"2026-05-05T01:33:58Z","published":null,"updated":"2026-05-09T17:38:32Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.20098990","type":"dois","attributes":{"doi":"10.5281/zenodo.20098990","identifiers":[{"identifier":"oai:zenodo.org:20098990","identifierType":"oai"}],"creators":[{"name":"Kenny, Cathal","nameType":"Personal","givenName":"Cathal","familyName":"Kenny","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"The Joseph Drive"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Solid state","subjectScheme":"GEMET"},{"subject":"Solid-state physics","subjectScheme":"EuroSciVoc"},{"subject":"Thorium/chemistry","subjectScheme":"MeSH"},{"subject":"Nano-materials","subjectScheme":"EuroSciVoc"},{"subject":"Cyclotron"},{"subject":"Terrawatt"},{"subject":"Quantum physics","subjectScheme":"EuroSciVoc"},{"subject":"Quantum field theory","subjectScheme":"EuroSciVoc"},{"subject":"Bose-einstein condensates","subjectScheme":"EuroSciVoc"},{"subject":"Metric System/history","subjectScheme":"MeSH"},{"subject":"Superfluids"},{"subject":"Microscopy, Atomic Force/methods","subjectScheme":"MeSH"}],"contributors":[],"dates":[{"date":"2026-05-09","dateType":"Issued"},{"date":"2026-06-02","dateType":"Created","dateInformation":"The Joseph Drive "}],"language":"en","types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.20098989","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"V27","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Preface:\n\n \n\nThe core objective of this paper is to bridge the gap between the lentz soliton model and real world engineering within the possibilities of a 2026 engineering budget, this theroy if proven would stand as a turning point in human history, officially marking the end traditional combustion era, and birth the start of the metric age, this would catapult humanity from a type .73 on the kardesheiv scale to the begining of type 1 status.\n\nThe Project ES1 craft utilizes a monolithic 1,500-tonne hull constructed from 152 million layers of alternating 0.5nm Bismuth and Magnesium nanolaminates. This \"Russian Doll\" architecture operates as a decentralized nodal network, where each atomic intersection facilitates thermoelectric Seebeck recovery and precise thermal stabilization. Maintained at a 3K superconducting state via an integrated liquid hydrogen capillary system, the hull functions as a rigid, Meissner-shielded frame that houses the dual-cyclotron floor panels and the lower-deck soliton containment vessel. This nodal design allows the ship to withstand extreme localized metric tilts while managing the 1.9 GW energy flux required for geodesic acceleration.\n\nThis document provides a comprehensive technical and mathematical audit of the Joseph Drive, beginning with the structural and material specifications of the nanolaminate hull. It details the mid-deck nuclear configuration, focusing on the pulsed cyclotron transmutation of Thorium-232 and the subsequent 38% mass-energy conversion. The central chapters present formal derivations of the Joseph Metric Tensor and the optomechanical guidance guideway, proving the stability of subluminal and superluminal transit within the laws of General Relativity and Quantum Chromodynamics. The treatise concludes with an operational safety analysis, addressing gravimetric tidal sensing, attosecond AI latency, and thermodynamic entropy rejection.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.20098990","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-05-09T17:22:21Z","registered":"2026-05-09T17:22:21Z","published":null,"updated":"2026-05-09T17:36:59Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.20098989","type":"dois","attributes":{"doi":"10.5281/zenodo.20098989","identifiers":[],"creators":[{"name":"Kenny, Cathal","nameType":"Personal","givenName":"Cathal","familyName":"Kenny","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"The Joseph Drive"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Solid state","subjectScheme":"GEMET"},{"subject":"Solid-state physics","subjectScheme":"EuroSciVoc"},{"subject":"Thorium/chemistry","subjectScheme":"MeSH"},{"subject":"Nano-materials","subjectScheme":"EuroSciVoc"},{"subject":"Cyclotron"},{"subject":"Terrawatt"},{"subject":"Quantum physics","subjectScheme":"EuroSciVoc"},{"subject":"Quantum field theory","subjectScheme":"EuroSciVoc"},{"subject":"Bose-einstein condensates","subjectScheme":"EuroSciVoc"},{"subject":"Metric System/history","subjectScheme":"MeSH"},{"subject":"Superfluids"},{"subject":"Microscopy, Atomic Force/methods","subjectScheme":"MeSH"}],"contributors":[],"dates":[{"date":"2026-05-09","dateType":"Issued"},{"date":"2026-06-02","dateType":"Created","dateInformation":"The Joseph Drive "}],"language":"en","types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.20098989","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"V27","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Preface:\n\n \n\nThe core objective of this paper is to bridge the gap between the lentz soliton model and real world engineering within the possibilities of a 2026 engineering budget, this theroy if proven would stand as a turning point in human history, officially marking the end traditional combustion era, and birth the start of the metric age, this would catapult humanity from a type .73 on the kardesheiv scale to the begining of type 1 status.\n\nThe Project ES1 craft utilizes a monolithic 1,500-tonne hull constructed from 152 million layers of alternating 0.5nm Bismuth and Magnesium nanolaminates. This \"Russian Doll\" architecture operates as a decentralized nodal network, where each atomic intersection facilitates thermoelectric Seebeck recovery and precise thermal stabilization. Maintained at a 3K superconducting state via an integrated liquid hydrogen capillary system, the hull functions as a rigid, Meissner-shielded frame that houses the dual-cyclotron floor panels and the lower-deck soliton containment vessel. This nodal design allows the ship to withstand extreme localized metric tilts while managing the 1.9 GW energy flux required for geodesic acceleration.\n\nThis document provides a comprehensive technical and mathematical audit of the Joseph Drive, beginning with the structural and material specifications of the nanolaminate hull. It details the mid-deck nuclear configuration, focusing on the pulsed cyclotron transmutation of Thorium-232 and the subsequent 38% mass-energy conversion. The central chapters present formal derivations of the Joseph Metric Tensor and the optomechanical guidance guideway, proving the stability of subluminal and superluminal transit within the laws of General Relativity and Quantum Chromodynamics. The treatise concludes with an operational safety analysis, addressing gravimetric tidal sensing, attosecond AI latency, and thermodynamic entropy rejection.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.20098989","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-05-09T17:22:21Z","registered":"2026-05-09T17:22:21Z","published":null,"updated":"2026-05-09T17:36:59Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.17964123","type":"dois","attributes":{"doi":"10.5281/zenodo.17964123","identifiers":[],"creators":[{"name":"Lindenhayn, Mark","nameType":"Personal","givenName":"Mark","familyName":"Lindenhayn","nameIdentifiers":[{"nameIdentifier":"0009-0008-6051-4114","nameIdentifierScheme":"ORCID"}],"affiliation":[]}],"titles":[{"title":"The Relational Nature of Zero-Dimensional Objects: Dimensional Closure, Scale Relativity, and Informational Leakage"}],"publisher":"Zenodo","container":{},"publicationYear":2025,"subjects":[{"subject":"Geometry","subjectScheme":"EuroSciVoc"},{"subject":"Topology","subjectScheme":"EuroSciVoc"},{"subject":"Information Theory","subjectScheme":"MeSH"},{"subject":"Scale invariance"},{"subject":"Physics","subjectScheme":"GEMET"},{"subject":"Mathematical physics","subjectScheme":"EuroSciVoc"},{"subject":"Mathematical logic","subjectScheme":"EuroSciVoc"},{"subject":"Mathematics","subjectScheme":"MeSH"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Quantum physics","subjectScheme":"EuroSciVoc"},{"subject":"Quantum Theory","subjectScheme":"MeSH"},{"subject":"Particle physics","subjectScheme":"EuroSciVoc"},{"subject":"Nuclear physics","subjectScheme":"EuroSciVoc"},{"subject":"Computational topology","subjectScheme":"EuroSciVoc"},{"subject":"Artificial intelligence","subjectScheme":"EuroSciVoc"},{"subject":"Machine learning","subjectScheme":"EuroSciVoc"},{"subject":"Machine Learning","subjectScheme":"MeSH"},{"subject":"Fractals","subjectScheme":"MeSH"}],"contributors":[],"dates":[{"date":"2025-12-17","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.17964123","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This paper explores the relational foundations of zero-dimensional objects in geometry, challenging the classical view of points as absolute primitives without extension or scale. We demonstrate that zero-dimensionality is inherently dependent on the ambient space's dimensional structure, introducing concepts such as dimensional closure (the ability to recover all geometric degrees of freedom intrinsically) and informational leakage (unaccounted freedoms in lower-dimensional embeddings). Through rigorous propositions and proofs grounded in topology, differential geometry, and information theory, we show that scale is relative and observer-dependent in lower dimensions, while higher-dimensional embeddings reveal hidden indeterminacies. This framework reframes dimensionality as a measure of informational capacity, resolving paradoxes like ultraviolet divergences, non-local correlations, and singularities in physics. Implications extend to alternative geometric foundations (e.g., categorical or information-theoretic) and emergent properties in quantum gravity. ","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.17964123","contentUrl":null,"metadataVersion":3,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":2,"versionOfCount":1,"created":"2025-12-17T14:02:14Z","registered":"2025-12-17T14:02:14Z","published":null,"updated":"2026-05-09T17:34:03Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.17964124","type":"dois","attributes":{"doi":"10.5281/zenodo.17964124","identifiers":[{"identifier":"oai:zenodo.org:17964124","identifierType":"oai"}],"creators":[{"name":"Lindenhayn, Mark","nameType":"Personal","givenName":"Mark","familyName":"Lindenhayn","nameIdentifiers":[{"nameIdentifier":"0009-0008-6051-4114","nameIdentifierScheme":"ORCID"}],"affiliation":[]}],"titles":[{"title":"The Relational Nature of Zero-Dimensional Objects: Dimensional Closure, Scale Relativity, and Informational Leakage"}],"publisher":"Zenodo","container":{},"publicationYear":2025,"subjects":[{"subject":"Geometry","subjectScheme":"EuroSciVoc"},{"subject":"Topology","subjectScheme":"EuroSciVoc"},{"subject":"Information Theory","subjectScheme":"MeSH"},{"subject":"Scale invariance"},{"subject":"Physics","subjectScheme":"GEMET"},{"subject":"Mathematical physics","subjectScheme":"EuroSciVoc"},{"subject":"Mathematical logic","subjectScheme":"EuroSciVoc"},{"subject":"Mathematics","subjectScheme":"MeSH"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Quantum physics","subjectScheme":"EuroSciVoc"},{"subject":"Quantum Theory","subjectScheme":"MeSH"},{"subject":"Particle physics","subjectScheme":"EuroSciVoc"},{"subject":"Nuclear physics","subjectScheme":"EuroSciVoc"},{"subject":"Computational topology","subjectScheme":"EuroSciVoc"},{"subject":"Artificial intelligence","subjectScheme":"EuroSciVoc"},{"subject":"Machine learning","subjectScheme":"EuroSciVoc"},{"subject":"Machine Learning","subjectScheme":"MeSH"},{"subject":"Fractals","subjectScheme":"MeSH"}],"contributors":[],"dates":[{"date":"2025-12-17","dateType":"Issued"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.17964123","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This paper explores the relational foundations of zero-dimensional objects in geometry, challenging the classical view of points as absolute primitives without extension or scale. We demonstrate that zero-dimensionality is inherently dependent on the ambient space's dimensional structure, introducing concepts such as dimensional closure (the ability to recover all geometric degrees of freedom intrinsically) and informational leakage (unaccounted freedoms in lower-dimensional embeddings). Through rigorous propositions and proofs grounded in topology, differential geometry, and information theory, we show that scale is relative and observer-dependent in lower dimensions, while higher-dimensional embeddings reveal hidden indeterminacies. This framework reframes dimensionality as a measure of informational capacity, resolving paradoxes like ultraviolet divergences, non-local correlations, and singularities in physics. Implications extend to alternative geometric foundations (e.g., categorical or information-theoretic) and emergent properties in quantum gravity. ","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.17964124","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2025-12-17T14:02:14Z","registered":"2025-12-17T14:02:14Z","published":null,"updated":"2026-05-09T17:34:03Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.17930722","type":"dois","attributes":{"doi":"10.5281/zenodo.17930722","identifiers":[],"creators":[{"name":"Lacueva Pérez, Francisco José","nameType":"Personal","givenName":"Francisco José","familyName":"Lacueva Pérez","affiliation":["Instituto Tecnológico de Aragón"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-0998-2939","nameIdentifierScheme":"ORCID"}]},{"name":"Labata Lezaun, Gorka","nameType":"Personal","givenName":"Gorka","familyName":"Labata Lezaun","affiliation":["Instituto Tecnológico de Aragón"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-8634-4124","nameIdentifierScheme":"ORCID"}]},{"name":"Ilarri, Sergio","nameType":"Personal","givenName":"Sergio","familyName":"Ilarri","affiliation":["Universidad de Zaragoza"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-7073-219X","nameIdentifierScheme":"ORCID"}]},{"name":"del Hoyo Alonso, Rafael","nameType":"Personal","givenName":"Rafael","familyName":"del Hoyo Alonso","affiliation":["Universidad San Jorge","Instituto Tecnológico de Aragón"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-2755-5500","nameIdentifierScheme":"ORCID"}]},{"name":"Barriuso Vargas, Juan","nameType":"Personal","givenName":"Juan","familyName":"Barriuso Vargas","affiliation":["Universidad de Zaragoza"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-2980-5454","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"A Multisource Grapevine Phenology Dataset for Smart Farming and AI Modeling"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Agri-foodstuff","subjectScheme":"GEMET"},{"subject":"Machine learning","subjectScheme":"EuroSciVoc"},{"subject":"Supervised Machine Learning","subjectScheme":"MeSH"},{"subject":"Agriculture","subjectScheme":"EuroSciVoc"}],"contributors":[{"name":"Ilarri, Sergio","nameType":"Personal","givenName":"Sergio","familyName":"Ilarri","affiliation":["Universidad de Zaragoza"],"contributorType":"Editor","nameIdentifiers":[{"nameIdentifier":"0000-0002-7073-219X","nameIdentifierScheme":"ORCID"}]},{"name":"Labata Lezaun, Gorka","nameType":"Personal","givenName":"Gorka","familyName":"Labata Lezaun","affiliation":["Instituto Tecnológico de Aragón"],"contributorType":"DataCurator","nameIdentifiers":[{"nameIdentifier":"0000-0002-8634-4124","nameIdentifierScheme":"ORCID"}]},{"name":"del Hoyo Alonso, Rafael","nameType":"Personal","givenName":"Rafael","familyName":"del Hoyo Alonso","affiliation":["Universidad San Jorge","Instituto Tecnológico de Aragón"],"contributorType":"DataCurator","nameIdentifiers":[{"nameIdentifier":"0000-0003-2755-5500","nameIdentifierScheme":"ORCID"}]},{"name":"Barriuso Vargas, Juan","nameType":"Personal","givenName":"Juan","familyName":"Barriuso Vargas","affiliation":["Universidad de Zaragoza"],"contributorType":"DataCurator","nameIdentifiers":[{"nameIdentifier":"0000-0003-2980-5454","nameIdentifierScheme":"ORCID"}]},{"name":"Lacueva Pérez, Francisco José","nameType":"Personal","givenName":"Francisco José","familyName":"Lacueva Pérez","affiliation":["Instituto Tecnológico de Aragón"],"contributorType":"ContactPerson","nameIdentifiers":[{"nameIdentifier":"0000-0003-0998-2939","nameIdentifierScheme":"ORCID"}]},{"name":"Ilarri, Sergio","nameType":"Personal","givenName":"Sergio","familyName":"Ilarri","affiliation":["Universidad de Zaragoza"],"contributorType":"DataManager","nameIdentifiers":[]}],"dates":[{"date":"2026-01-10","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1016/j.compag.2025.110018","resourceTypeGeneral":"JournalArticle","relatedIdentifierType":"DOI"},{"relationType":"IsReferencedBy","relatedIdentifier":"https://webdiis.unizar.es/~silarri/prot/DREAM/index.html","resourceTypeGeneral":"Other","relatedIdentifierType":"URL"},{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.17930722","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1.0","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Description\n\nArtificial Intelligence and Machine Learning rely on large, high-quality datasets for accurate and robust models, yet data scarcity remains a major challenge—especially in smart farming. Agricultural data are highly diverse and heterogeneous, complicating model development. Phenology modeling, a key application, studies how plant biological events relate to climate and seasons. Accurate phenology models improve crop quality, support climate adaptation, and guide decisions such as pesticide use and harvesting, enhancing environmental and economic sustainability.This study introduces a georeferenced dataset for Machine Learning-based grapevine phenology prediction across 3 Protected Designations of Origin in Arag’on, Spain. Developed by a multidisciplinary team, the dataset combines 9 datasets from 8 sources—including meteorological time series, field phenology observations, and Copernicus Sentinel-2 multispectral imagery—covering the period 2016–2022. It supports both physical and ML-based phenology modeling and facilitates knowledge extraction in agronomy and plant biology. Its relevance lies in its comprehensive scope, the inclusion of 9 phenological stages, and a rigorous methodology ensuring reproducibility. This framework enables the creation of similar datasets for otherregions or crops, advancing smart farming through scalable, data-driven solutions. We further anticipate its potential contribution to developing Foundation Models as well as to the creation.\n\nDataset Structure\n\nThe dataset contains 2 main CSV files and 1 supporting folder:\n\n\n\nDIF Description.pdf- Description of the data set.\n\nDIF phenologicaletages.csv: it contains the links from the phenologystageid values in the file “DIF GrapevinePehologyDataset.csv:  with the correspoding BBCH values.\n\nDIF GrapevinePehologyDataset.csv: it contains the dataset used to train the models we presented.\n\nMetadata: this folder contains the JSON files describing the dataset and its content:\n\n\n\nDIF_DataSetDescription.json: the description contained if “DIF Description.pdf” but in JSON format.\n\nDIF GrapevinePehologyDataset.json: contains the dataset presented in this paper which was used to train the models we presented in [51, 50]. We describe the content of the file in next paragraphs.\n\n\n\n\nIntended Use\n\nThe goal of this dataset is to enable the development of models for predicting grapevine phenology in the three Protected Designations of Origin in Aragón (Spain), using data from field observations, meteorological stations, and NDVI derived from Copernicus Sentinel-2 multispectral imagery. Additionally, it supports the calibration of physical models for these regions by including the calculation of cold and heat accumulation indices. These calculations are performed using the traditional start dates of January 1 and February of the corresponding year, as well as from the date when plants enter dormancy: the first autumn day when the maximum temperature does not exceed 10 °C.\n\nAccess Conditions: This dataset is publicly available under the terms of the Creative Commons Attribution 4.0 International license. \n\nSpecifications Table\n\n\n\n\n\n\nSubject\n\n\n\nSmart farming\n\n\n\n\n\nSpecific subject area\n\n\n\nThe dataset is based on data from 3 Protected Designations of Origin—Calatayud, Cariñena and Campo de Borja, —in Aragón, northeastern Spain. Built by merging 9 georeferenced time-series datasets from 8 data sources considering the period from 2016 to 2022. It includes meteorological data (measurements, estimates, and forecasts), qualitative field phenology observations, and Copernicus Sentinel-2 multispectral imagery.\n\n\n\n\n\nType of data\n\n\n\nAnalyzedFilteredProcessedMulti-source\n\n\n\n\n\nData collection\n\n\n\nData merged in the dataset is obtained from 9 georeferenced datatsets obtained from 8 data sources.  The datasets considered are:\n\n·         Red FARA phenological registry [1]: this dataset has restricted access.  It provides phenology field observations on the control parcels.\n\n·         Spanish Cadastral Registry (Catastro) [2]: it is used to normalize Red FARA records and to obtain the NDVI of the control parcels from Copernicus Sentinel 2 images.\n\n·         Aragón Open Data Common Agrarian Policy Registry (CAP) [3]: together with the Catastro data is used to normalize of the Red FARA records.\n\n·         SIAR [4] and Grapevine [5] climatic station networks provide meteorological data.\n\n·         ERA5 real climatic estimations [6] and ECMWF IFS forecast [7] data used to replace failures in climatic data and forecast data to perform predictions.\n\n·         Copernicus Sentinel 2 multispectral images [8]: these images are used to determine the NDVI of the control parcels used to create the dataset. \n\nFor accessing these dataset we used available APIs.  All they are public and provide open access. The 2 exceptions were Red FARA which has restricted access, and ERA5 data which was accessed using openMeteo API [8] which eased our work.  The access to the data and the transformations performed in them were coded in Python.  A deep explanation of the transformation performed can be obtained in [9].\n\n\n\n\n\nData source location\n\n\n\nCountry: Spain.Region: Aragón.Protected Designation of Origin: Calatayud, Campo de Borja, Cariñena.Coordinates: Parallelepiped defined by points (41.98107, −2.177578) and (41.166320, −0.922575) in WGS84 coordinates.\n\n\n\n\n\nData accessibility\n\n\n\nRepository name: Zenodo\n\nData identification number: 10.5281/zenodo.17930723 Direct URL to data: https://doi.org/10.5281/zenodo.17930723  \n\n\n\n\n\nReferences\n\n\n\n\n\n\n\n\n[1]\n\n\n\nGovernment of Aragón. Red FARA Home Page. Last access: January 10, 2026. 2026. url: http://web.redfara.es.\n\n\n\n\n\n[2]\n\n\n\nSpanish Treasury. Spanish Cadastral Registry Electronic Home Page. Last access: January 10, 2026. 2026. url: https://www.sedecatastro.gob.es/.\n\n\n\n\n\n[3]\n\n\n\nGovernment of Aragón. Aragón Open Data Home Page. Last access: January 10, 2026. 2026. url: https://opendata.aragon.es.\n\n\n\n\n\n[4]\n\n\n\nSpanish Ministry of Agriculture, Fisheries and Food. Agro-climatic Information System for Irrigation (SIAR) Home Page. Last access: January 10, 2026. 2026. url: https://eportal.mapa.gob.es//websiar/Inicio.aspx .\n\n\n\n\n\n[5]\n\n\n\nGrapevine Project Consortium. Grapevine Project Home Page. Grant agreement ID: 863463. https://grapevine-project.eu (Last access: August 8, 2024), https://web.archive.org/web/20230922054033/https://grapevine- project.eu (Last access: January 10, 2026), https://www.egi.eu/case-study/grapevine (Last access: January 10, 2026). 2022.\n\n\n\n\n\n[6]\n\n\n\nCopernicus Climate Change Service (C3S). ERA5 hourly data on single levels from 1959 to present. Last access: January 10, 2026. 2023. url: https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview .\n\n\n\n\n\n[7]\n\n\n\nEuropean Centre for Medium-Range Weather Forecasts (ECMWF). ECMWF Open Data. Last accessed January 10, 2026. 2026. url: https://www.ecmwf.int/en/forecasts/datasets/open-data .\n\n\n\n\n\n[8]\n\n\n\nF. Gascon et al. “Copernicus Sentinel-2 mission: products, algorithms and Cal/Val”. In: Earth Observing Systems XIX. Ed. by James J. Butler, Xiaoxiong (Jack) Xiong, and Xingfa Gu.SPIE, Sept. 2014, pp. 1–9. doi:10.1117/12.2062260. url: http://dx.doi.org/10.1117/12.2062260.\n\n\n\n\n\n[9]\n\n\n\nFrancisco Jos´e Lacueva-P´erez et al. “Developing machine learning models from multisourced real-world datasets to enhance smart-farming practices”. In: Computers and Electronics in Agriculture 231 (Apr. 2025), p. 110018. issn: 0168-1699. doi: 10.1016/j.compag.2025.110018. url: http://dx.doi.org/10.1016/j.compag.2025.110018 .\n\n\n\n\n\n \n\n\n\n\n\n \n\n \n\n\n\n\n \n\nFile “DIF_GrapevinePehologyDataset.csv” Description\n\nFile DIF_GrapevinePehologyDataset.csv contains the dataset presented in this paper.  Each of the records represents the data considered for a given parcel (vineyard) in each date. The following table provides a description of the fields contained in the dataset. For clarity, we simplified the table by using an abbreviated notation for the field names; specifically, for some field names we include an asterisk (“*”) with the name followed by a couple of numbers in brackets (“[…]”) that describe the range of integer values that can replace the “*” in the dataset; for example, we did this in fields which provide values of the given variable data for the n days before (days_after ) and after (days_adelante).  For clarity, we provide here some examples:\n\n·         tmed_min *_days_after  [1,13]: this name represents that the dataset contains all the fields tmed_min 1_days_after , tmed_min 2_days_after , ..., tmed_min 13_days_after , which represent, for the given field, the minimum temperature for each of the n days before the date of the record.\n\n·         wind_NE *_days_after [1,6]: this name represents that the dataset contains all the fields wind_NE 1_days_after, t wind_NE 2_days_after, ..., wind_NE 6_days_after, which represent, for the given field, the wind_NE index for each of the n days after the date of the record.\n\n·         gdd_4.5_t0_Tbase_sum *_weeks_before [1,2]: this name represents that the dataset contains all the fields gdd_4.5_t0_Tbase_sum 1_weeks_before and gdd_4.5_t0_Tbase_sum 2_weeks_before, which represent, for the given field, the GDD calculated using the base temperature 4.5º C and starting to accumulate at the beginning of the session.\n\nMoreover, we use “|” to denote choices (expressed within brackets “[…]”), which can represent several attributes. For example, “rad_[min|MAX|mean]” actually represents (in a condensed way) 3 different variables: “rad_min”, “rad_max” and “rad_mean”. Other notations can be interpreted similarly. The full list of variable names is shown in Appendix A.\n\n\n\n\n\n\nField Name (abbreviated notation)\n\n\n\nDescription\n\n\n\n\n\n\n\nphenologystageid\n\n\n\nId of the phenological stage of the parcel on the given date.  See file “DIF phenologicalstages.csv”.\n\n\n\n\n\nvariety\n\n\n\nGrapevine variety:  Cabernet Sauvignon, Chardonnay, Garnacha, Mazuela, Syrach, Tempranillo.\n\n\n\n\n\ncodigo\n\n\n\nId of the parcel in the Spanish Cadastral Registry.\n\n\n\n\n\nlongitude\n\n\n\nLongitude of the centroid of the parcel.\n\n\n\n\n\nlatitude\n\n\n\nLatitude of the centroid of the parcel.\n\n\n\n\n\naltitudeASL\n\n\n\nAltitudeASL of the centroid of the parcel.\n\n\n\n\n\nPDO_id\n\n\n\nId of the Protected Designation of Origin (PDO): Calatayud, Carinena and Campo de Borja.\n\n\n\n\n\ndate\n\n\n\nThe date of the record.\n\n\n\n\n\nstation\n\n\n\nThe name of the climatic station whose data are considered.\n\n\n\n\n\nseason\n\n\n\nThe season to which the record belongs.\n\n\n\n\n\nday\n\n\n\nThe DOY (day of the year).\n\n\n\n\n\n\"PDO_Borja\", \"PDO_Calatayud\", \"PDO_Carinena\", \"PDO_Somontano\"\n\n\n\nBoolean values which are true when the record corresponds to the given PDO.\n\n\n\n\n\n\"variety_CABERNET SAUVIGNON\", \"variety_CHARDONNAY\", \"variety_GARNACHA\", \"variety_MAZUELA\", \"variety_SYRACH\", \"variety_TEMPRANILLO\"\n\n\n\nBoolean values which are true when the record corresponds to a field with the given variety.\n\n \n\n\n\n\n\nmin, MAX, mean, std, medayn, diff\n\n\n\nValues derived from the NDVI indexes calculated for each parcel from the Copernicus Sentinel 2 multispectral images.  They represent the minimum, maximum, average, standard deviation, medayn and difference values.\n\n\n\n\n\ntmed_[min|MAX|mean]\n\n\n\n[Minimum|Maximum|Mean] temperature for the given date (ºC).\n\n\n\n\n\ntmed_[min|MAX|mean] *_days_after  [1,13]\n\n\n\n[Minimum|Maximum|Mean] temperatures for the 13 days before the given date.\n\n\n\n\n\ntmed_[min|MAX|mean] *_days_after [1,6]\n\n\n\n[Minimum|Maximum|Mean] temperatures for the 6 days following the given date.\n\n\n\n\n\nrad_[min|MAX|mean]\n\n\n\n[Minimum|Maximum|Mean] radaytion for the given date (W/m²).\n\n\n\n\n\nrad_[min|MAX|mean] *_days_after  [1,13]\n\n\n\n[Minimum|Maximum|Mean] radaytion for the 13 days before the given date.\n\n\n\n\n\nrad_[min|MAX|mean] *_days_after [1,6]\n\n\n\n[Minimum|Maximum|Mean] radaytion for the 6 days following the given date.\n\n\n\n\n\nhr_ mean\n\n\n\nAverage air relative humidity for the given date (%).\n\n\n\n\n\nhr_mean *_days_after  [1,13]\n\n\n\nAverage air relative humidity radaytion for the 13 days before the given date.\n\n\n\n\n\nhr_mean *_days_after [1,6]\n\n\n\nAverage air relative humidity radaytion for the 6 days following the given date.\n\n\n\n\n\nwind_[N|NE|E|SE|S|SW|W|NW] *_days_after  [1,13]\n\n\n\nWind index for the North, North-East, East, South-East, South, South-West, West, North-West area for the 13 days before the given date.\n\n\n\n\n\nind_[N|NE|E|SE|S|SW|W|NW] *_days_after [1,6]\n\n\n\nWind index for the North, North-East, East, South-East, South, South-West, West, North-West area for the 6 days following the given date.\n\n\n\n\n\ngdd_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum\n\n\n\nGDD heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum).\n\n\n\n\n\ngdd_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum *_weeks_before [1|2]\n\n\n\nGDD heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the 2 weeks previous to the given day.\n\n\n\n\n\ngdd_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum * 1_weeks_after\n\n\n\nGDD heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the next week to the given day.\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_[TBase|Tbasemin]_sum\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum).\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_[TBase|Tbasemin]_sum *_weeks_before [1|2]\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the 2 weeks previous to the given day.\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_[TBase|Tbasemin]_sum * 1_weeks_after\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the next week to the given day.\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_ Utah _sum\n\n\n\nUtah cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum).\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_ Utah _sum *_weeks_before [1|2]\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the 2 weeks previous to the given day.\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_ Utah _sum * 1_weeks_after\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the next week to the given day.\n\n\n\n\n\nrad_sum\n\n\n\nAccumulated radaytion since the beginning of the season until the given date.\n\n\n\n\n\nrad_sum *_weeks_before [1|2]\n\n\n\nAccumulated radaytion since the beginning of the season until 1 or 2 weeks before the given date.\n\n\n\n\n\nrad_sum 1_weeks_after\n\n\n\nAccumulated radaytion since the beginning of the season until the next week after the given date.\n\n\n\n\n\nprecip_sum\n\n\n\nAccumulated precipitation since the beginning of the season until the given date.\n\n\n\n\n\nprecip_sum *_weeks_before [1|2]\n\n\n\nAccumulated precipitation since the beginning of the season until 1 or 2 weeks before the given date.\n\n\n\n\n\nprecip_sum 1_weeks_after\n\n\n\nAccumulated precipitation since the beginning of the season until the next week after the given date.\n\n\n\n\n\nwinkler_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum\n\n\n\nWinkler heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum).\n\n\n\n\n\nwinkler_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum *_weeks_before [1|2]\n\n\n\nWinkler heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the 2 weeks previous to the given day.\n\n\n\n\n\nwinkler_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum * 1_weeks_after\n\n\n\nWinkler heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the next week after the given day.\n\n\n\n\n\n \n\n \n\nThe GDD, Winkler and Chilling (Richardson and Utah) indexes are also calculated considering the contributions of the time units (periods) to the daily contribution. These fields (or columns of the file) have the same naming schema as their counterparts based on daily calculations but with the “cumm” suffix.\n\nFile “DIF phenologicalstages.csv” Description\n\nThis file contains a description of the different types of phenological stages considered. The fields are:\n\n\n\nitainnovaid: this is an identifier of the phenological stage.\n\nbbch: the number of stage in the BBCH phenological stage.\n\nDescripción BBCH: this is a textual description of the previous BBCH phenological stage.\n\n\nThe contents of the file are as follows:\n\n \n\n\n\n\n\n\nitainnovaid\n\n\n\nbbch\n\n\n\nDescripción BBCH\n\n\n\n\n\n0\n\n\n\n0\n\n\n\nWinter dormancy or resting period\n\n\n\n\n\n3\n\n\n\n63\n\n\n\nEarly flowering: 30% of flowerhoods fallen\n\n\n\n\n\n1\n\n\n\n11\n\n\n\nFirst leaf unfolded and spread away from shoot\n\n\n\n\n\n2\n\n\n\n15\n\n\n\n5 leaves unfolded\n\n\n\n\n\n4\n\n\n\n65\n\n\n\nFull flowering: 50% of flowerhoods fallen\n\n\n\n\n\n6\n\n\n\n71\n\n\n\nFruit set: young fruits begin to swell, remains of flowers\n\n\n\n\n\n5\n\n\n\n68\n\n\n\n80% of flowerhoods fallen\n\n\n\n\n\n7\n\n\n\n75\n\n\n\n50% of fruits have reached final size or fruit has reached 50% of final size\n\n\n\n\n\n8\n\n\n\n77\n\n\n\n70% of fruits have reached final size or fruit has reached 70% of final size\n\n\n\n\n\n9\n\n\n\n81\n\n\n\nBeginning of ripening or fruit colouration","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"awardTitle":"NEAT-AMBIENCE - Next-gEnerATion dAta Management to foster suitable Behaviors and the resilience of cItizens against modErN ChallEnges","funderName":"Agencia Estatal de Investigación","awardNumber":"PID2020-113037RB-I00","funderIdentifier":"10.13039/501100011033","funderIdentifierType":"Crossref Funder ID"},{"awardTitle":"COSMOS, Computer Science for Complex System modelling","funderName":"Gobierno de Aragón","awardNumber":"T64_23R","funderIdentifier":"10.13039/501100010067","funderIdentifierType":"Crossref Funder ID"}],"url":"https://zenodo.org/doi/10.5281/zenodo.17930722","contentUrl":null,"metadataVersion":14,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":2,"versionOfCount":1,"created":"2026-04-08T17:59:11Z","registered":"2026-04-08T17:59:11Z","published":null,"updated":"2026-05-09T17:34:01Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.17930723","type":"dois","attributes":{"doi":"10.5281/zenodo.17930723","identifiers":[{"identifier":"oai:zenodo.org:17930723","identifierType":"oai"}],"creators":[{"name":"Lacueva Pérez, Francisco José","nameType":"Personal","givenName":"Francisco José","familyName":"Lacueva Pérez","affiliation":["Instituto Tecnológico de Aragón"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-0998-2939","nameIdentifierScheme":"ORCID"}]},{"name":"Labata Lezaun, Gorka","nameType":"Personal","givenName":"Gorka","familyName":"Labata Lezaun","affiliation":["Instituto Tecnológico de Aragón"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-8634-4124","nameIdentifierScheme":"ORCID"}]},{"name":"Ilarri, Sergio","nameType":"Personal","givenName":"Sergio","familyName":"Ilarri","affiliation":["Universidad de Zaragoza"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-7073-219X","nameIdentifierScheme":"ORCID"}]},{"name":"del Hoyo Alonso, Rafael","nameType":"Personal","givenName":"Rafael","familyName":"del Hoyo Alonso","affiliation":["Universidad San Jorge","Instituto Tecnológico de Aragón"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-2755-5500","nameIdentifierScheme":"ORCID"}]},{"name":"Barriuso Vargas, Juan","nameType":"Personal","givenName":"Juan","familyName":"Barriuso Vargas","affiliation":["Universidad de Zaragoza"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-2980-5454","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"A Multisource Grapevine Phenology Dataset for Smart Farming and AI Modeling"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Agri-foodstuff","subjectScheme":"GEMET"},{"subject":"Machine learning","subjectScheme":"EuroSciVoc"},{"subject":"Supervised Machine Learning","subjectScheme":"MeSH"},{"subject":"Agriculture","subjectScheme":"EuroSciVoc"}],"contributors":[{"name":"Ilarri, Sergio","nameType":"Personal","givenName":"Sergio","familyName":"Ilarri","affiliation":["Universidad de Zaragoza"],"contributorType":"Editor","nameIdentifiers":[{"nameIdentifier":"0000-0002-7073-219X","nameIdentifierScheme":"ORCID"}]},{"name":"Labata Lezaun, Gorka","nameType":"Personal","givenName":"Gorka","familyName":"Labata Lezaun","affiliation":["Instituto Tecnológico de Aragón"],"contributorType":"DataCurator","nameIdentifiers":[{"nameIdentifier":"0000-0002-8634-4124","nameIdentifierScheme":"ORCID"}]},{"name":"del Hoyo Alonso, Rafael","nameType":"Personal","givenName":"Rafael","familyName":"del Hoyo Alonso","affiliation":["Universidad San Jorge","Instituto Tecnológico de Aragón"],"contributorType":"DataCurator","nameIdentifiers":[{"nameIdentifier":"0000-0003-2755-5500","nameIdentifierScheme":"ORCID"}]},{"name":"Barriuso Vargas, Juan","nameType":"Personal","givenName":"Juan","familyName":"Barriuso Vargas","affiliation":["Universidad de Zaragoza"],"contributorType":"DataCurator","nameIdentifiers":[{"nameIdentifier":"0000-0003-2980-5454","nameIdentifierScheme":"ORCID"}]},{"name":"Lacueva Pérez, Francisco José","nameType":"Personal","givenName":"Francisco José","familyName":"Lacueva Pérez","affiliation":["Instituto Tecnológico de Aragón"],"contributorType":"ContactPerson","nameIdentifiers":[{"nameIdentifier":"0000-0003-0998-2939","nameIdentifierScheme":"ORCID"}]},{"name":"Ilarri, Sergio","nameType":"Personal","givenName":"Sergio","familyName":"Ilarri","affiliation":["Universidad de Zaragoza"],"contributorType":"DataManager","nameIdentifiers":[]}],"dates":[{"date":"2026-01-10","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1016/j.compag.2025.110018","resourceTypeGeneral":"JournalArticle","relatedIdentifierType":"DOI"},{"relationType":"IsReferencedBy","relatedIdentifier":"https://webdiis.unizar.es/~silarri/prot/DREAM/index.html","resourceTypeGeneral":"Other","relatedIdentifierType":"URL"},{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.17930722","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1.0","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Description\n\nArtificial Intelligence and Machine Learning rely on large, high-quality datasets for accurate and robust models, yet data scarcity remains a major challenge—especially in smart farming. Agricultural data are highly diverse and heterogeneous, complicating model development. Phenology modeling, a key application, studies how plant biological events relate to climate and seasons. Accurate phenology models improve crop quality, support climate adaptation, and guide decisions such as pesticide use and harvesting, enhancing environmental and economic sustainability.This study introduces a georeferenced dataset for Machine Learning-based grapevine phenology prediction across 3 Protected Designations of Origin in Arag’on, Spain. Developed by a multidisciplinary team, the dataset combines 9 datasets from 8 sources—including meteorological time series, field phenology observations, and Copernicus Sentinel-2 multispectral imagery—covering the period 2016–2022. It supports both physical and ML-based phenology modeling and facilitates knowledge extraction in agronomy and plant biology. Its relevance lies in its comprehensive scope, the inclusion of 9 phenological stages, and a rigorous methodology ensuring reproducibility. This framework enables the creation of similar datasets for otherregions or crops, advancing smart farming through scalable, data-driven solutions. We further anticipate its potential contribution to developing Foundation Models as well as to the creation.\n\nDataset Structure\n\nThe dataset contains 2 main CSV files and 1 supporting folder:\n\n\n\nDIF Description.pdf- Description of the data set.\n\nDIF phenologicaletages.csv: it contains the links from the phenologystageid values in the file “DIF GrapevinePehologyDataset.csv:  with the correspoding BBCH values.\n\nDIF GrapevinePehologyDataset.csv: it contains the dataset used to train the models we presented.\n\nMetadata: this folder contains the JSON files describing the dataset and its content:\n\n\n\nDIF_DataSetDescription.json: the description contained if “DIF Description.pdf” but in JSON format.\n\nDIF GrapevinePehologyDataset.json: contains the dataset presented in this paper which was used to train the models we presented in [51, 50]. We describe the content of the file in next paragraphs.\n\n\n\n\nIntended Use\n\nThe goal of this dataset is to enable the development of models for predicting grapevine phenology in the three Protected Designations of Origin in Aragón (Spain), using data from field observations, meteorological stations, and NDVI derived from Copernicus Sentinel-2 multispectral imagery. Additionally, it supports the calibration of physical models for these regions by including the calculation of cold and heat accumulation indices. These calculations are performed using the traditional start dates of January 1 and February of the corresponding year, as well as from the date when plants enter dormancy: the first autumn day when the maximum temperature does not exceed 10 °C.\n\nAccess Conditions: This dataset is publicly available under the terms of the Creative Commons Attribution 4.0 International license. \n\nSpecifications Table\n\n\n\n\n\n\nSubject\n\n\n\nSmart farming\n\n\n\n\n\nSpecific subject area\n\n\n\nThe dataset is based on data from 3 Protected Designations of Origin—Calatayud, Cariñena and Campo de Borja, —in Aragón, northeastern Spain. Built by merging 9 georeferenced time-series datasets from 8 data sources considering the period from 2016 to 2022. It includes meteorological data (measurements, estimates, and forecasts), qualitative field phenology observations, and Copernicus Sentinel-2 multispectral imagery.\n\n\n\n\n\nType of data\n\n\n\nAnalyzedFilteredProcessedMulti-source\n\n\n\n\n\nData collection\n\n\n\nData merged in the dataset is obtained from 9 georeferenced datatsets obtained from 8 data sources.  The datasets considered are:\n\n·         Red FARA phenological registry [1]: this dataset has restricted access.  It provides phenology field observations on the control parcels.\n\n·         Spanish Cadastral Registry (Catastro) [2]: it is used to normalize Red FARA records and to obtain the NDVI of the control parcels from Copernicus Sentinel 2 images.\n\n·         Aragón Open Data Common Agrarian Policy Registry (CAP) [3]: together with the Catastro data is used to normalize of the Red FARA records.\n\n·         SIAR [4] and Grapevine [5] climatic station networks provide meteorological data.\n\n·         ERA5 real climatic estimations [6] and ECMWF IFS forecast [7] data used to replace failures in climatic data and forecast data to perform predictions.\n\n·         Copernicus Sentinel 2 multispectral images [8]: these images are used to determine the NDVI of the control parcels used to create the dataset. \n\nFor accessing these dataset we used available APIs.  All they are public and provide open access. The 2 exceptions were Red FARA which has restricted access, and ERA5 data which was accessed using openMeteo API [8] which eased our work.  The access to the data and the transformations performed in them were coded in Python.  A deep explanation of the transformation performed can be obtained in [9].\n\n\n\n\n\nData source location\n\n\n\nCountry: Spain.Region: Aragón.Protected Designation of Origin: Calatayud, Campo de Borja, Cariñena.Coordinates: Parallelepiped defined by points (41.98107, −2.177578) and (41.166320, −0.922575) in WGS84 coordinates.\n\n\n\n\n\nData accessibility\n\n\n\nRepository name: Zenodo\n\nData identification number: 10.5281/zenodo.17930723 Direct URL to data: https://doi.org/10.5281/zenodo.17930723  \n\n\n\n\n\nReferences\n\n\n\n\n\n\n\n\n[1]\n\n\n\nGovernment of Aragón. Red FARA Home Page. Last access: January 10, 2026. 2026. url: http://web.redfara.es.\n\n\n\n\n\n[2]\n\n\n\nSpanish Treasury. Spanish Cadastral Registry Electronic Home Page. Last access: January 10, 2026. 2026. url: https://www.sedecatastro.gob.es/.\n\n\n\n\n\n[3]\n\n\n\nGovernment of Aragón. Aragón Open Data Home Page. Last access: January 10, 2026. 2026. url: https://opendata.aragon.es.\n\n\n\n\n\n[4]\n\n\n\nSpanish Ministry of Agriculture, Fisheries and Food. Agro-climatic Information System for Irrigation (SIAR) Home Page. Last access: January 10, 2026. 2026. url: https://eportal.mapa.gob.es//websiar/Inicio.aspx .\n\n\n\n\n\n[5]\n\n\n\nGrapevine Project Consortium. Grapevine Project Home Page. Grant agreement ID: 863463. https://grapevine-project.eu (Last access: August 8, 2024), https://web.archive.org/web/20230922054033/https://grapevine- project.eu (Last access: January 10, 2026), https://www.egi.eu/case-study/grapevine (Last access: January 10, 2026). 2022.\n\n\n\n\n\n[6]\n\n\n\nCopernicus Climate Change Service (C3S). ERA5 hourly data on single levels from 1959 to present. Last access: January 10, 2026. 2023. url: https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview .\n\n\n\n\n\n[7]\n\n\n\nEuropean Centre for Medium-Range Weather Forecasts (ECMWF). ECMWF Open Data. Last accessed January 10, 2026. 2026. url: https://www.ecmwf.int/en/forecasts/datasets/open-data .\n\n\n\n\n\n[8]\n\n\n\nF. Gascon et al. “Copernicus Sentinel-2 mission: products, algorithms and Cal/Val”. In: Earth Observing Systems XIX. Ed. by James J. Butler, Xiaoxiong (Jack) Xiong, and Xingfa Gu.SPIE, Sept. 2014, pp. 1–9. doi:10.1117/12.2062260. url: http://dx.doi.org/10.1117/12.2062260.\n\n\n\n\n\n[9]\n\n\n\nFrancisco Jos´e Lacueva-P´erez et al. “Developing machine learning models from multisourced real-world datasets to enhance smart-farming practices”. In: Computers and Electronics in Agriculture 231 (Apr. 2025), p. 110018. issn: 0168-1699. doi: 10.1016/j.compag.2025.110018. url: http://dx.doi.org/10.1016/j.compag.2025.110018 .\n\n\n\n\n\n \n\n\n\n\n\n \n\n \n\n\n\n\n \n\nFile “DIF_GrapevinePehologyDataset.csv” Description\n\nFile DIF_GrapevinePehologyDataset.csv contains the dataset presented in this paper.  Each of the records represents the data considered for a given parcel (vineyard) in each date. The following table provides a description of the fields contained in the dataset. For clarity, we simplified the table by using an abbreviated notation for the field names; specifically, for some field names we include an asterisk (“*”) with the name followed by a couple of numbers in brackets (“[…]”) that describe the range of integer values that can replace the “*” in the dataset; for example, we did this in fields which provide values of the given variable data for the n days before (days_after ) and after (days_adelante).  For clarity, we provide here some examples:\n\n·         tmed_min *_days_after  [1,13]: this name represents that the dataset contains all the fields tmed_min 1_days_after , tmed_min 2_days_after , ..., tmed_min 13_days_after , which represent, for the given field, the minimum temperature for each of the n days before the date of the record.\n\n·         wind_NE *_days_after [1,6]: this name represents that the dataset contains all the fields wind_NE 1_days_after, t wind_NE 2_days_after, ..., wind_NE 6_days_after, which represent, for the given field, the wind_NE index for each of the n days after the date of the record.\n\n·         gdd_4.5_t0_Tbase_sum *_weeks_before [1,2]: this name represents that the dataset contains all the fields gdd_4.5_t0_Tbase_sum 1_weeks_before and gdd_4.5_t0_Tbase_sum 2_weeks_before, which represent, for the given field, the GDD calculated using the base temperature 4.5º C and starting to accumulate at the beginning of the session.\n\nMoreover, we use “|” to denote choices (expressed within brackets “[…]”), which can represent several attributes. For example, “rad_[min|MAX|mean]” actually represents (in a condensed way) 3 different variables: “rad_min”, “rad_max” and “rad_mean”. Other notations can be interpreted similarly. The full list of variable names is shown in Appendix A.\n\n\n\n\n\n\nField Name (abbreviated notation)\n\n\n\nDescription\n\n\n\n\n\n\n\nphenologystageid\n\n\n\nId of the phenological stage of the parcel on the given date.  See file “DIF phenologicalstages.csv”.\n\n\n\n\n\nvariety\n\n\n\nGrapevine variety:  Cabernet Sauvignon, Chardonnay, Garnacha, Mazuela, Syrach, Tempranillo.\n\n\n\n\n\ncodigo\n\n\n\nId of the parcel in the Spanish Cadastral Registry.\n\n\n\n\n\nlongitude\n\n\n\nLongitude of the centroid of the parcel.\n\n\n\n\n\nlatitude\n\n\n\nLatitude of the centroid of the parcel.\n\n\n\n\n\naltitudeASL\n\n\n\nAltitudeASL of the centroid of the parcel.\n\n\n\n\n\nPDO_id\n\n\n\nId of the Protected Designation of Origin (PDO): Calatayud, Carinena and Campo de Borja.\n\n\n\n\n\ndate\n\n\n\nThe date of the record.\n\n\n\n\n\nstation\n\n\n\nThe name of the climatic station whose data are considered.\n\n\n\n\n\nseason\n\n\n\nThe season to which the record belongs.\n\n\n\n\n\nday\n\n\n\nThe DOY (day of the year).\n\n\n\n\n\n\"PDO_Borja\", \"PDO_Calatayud\", \"PDO_Carinena\", \"PDO_Somontano\"\n\n\n\nBoolean values which are true when the record corresponds to the given PDO.\n\n\n\n\n\n\"variety_CABERNET SAUVIGNON\", \"variety_CHARDONNAY\", \"variety_GARNACHA\", \"variety_MAZUELA\", \"variety_SYRACH\", \"variety_TEMPRANILLO\"\n\n\n\nBoolean values which are true when the record corresponds to a field with the given variety.\n\n \n\n\n\n\n\nmin, MAX, mean, std, medayn, diff\n\n\n\nValues derived from the NDVI indexes calculated for each parcel from the Copernicus Sentinel 2 multispectral images.  They represent the minimum, maximum, average, standard deviation, medayn and difference values.\n\n\n\n\n\ntmed_[min|MAX|mean]\n\n\n\n[Minimum|Maximum|Mean] temperature for the given date (ºC).\n\n\n\n\n\ntmed_[min|MAX|mean] *_days_after  [1,13]\n\n\n\n[Minimum|Maximum|Mean] temperatures for the 13 days before the given date.\n\n\n\n\n\ntmed_[min|MAX|mean] *_days_after [1,6]\n\n\n\n[Minimum|Maximum|Mean] temperatures for the 6 days following the given date.\n\n\n\n\n\nrad_[min|MAX|mean]\n\n\n\n[Minimum|Maximum|Mean] radaytion for the given date (W/m²).\n\n\n\n\n\nrad_[min|MAX|mean] *_days_after  [1,13]\n\n\n\n[Minimum|Maximum|Mean] radaytion for the 13 days before the given date.\n\n\n\n\n\nrad_[min|MAX|mean] *_days_after [1,6]\n\n\n\n[Minimum|Maximum|Mean] radaytion for the 6 days following the given date.\n\n\n\n\n\nhr_ mean\n\n\n\nAverage air relative humidity for the given date (%).\n\n\n\n\n\nhr_mean *_days_after  [1,13]\n\n\n\nAverage air relative humidity radaytion for the 13 days before the given date.\n\n\n\n\n\nhr_mean *_days_after [1,6]\n\n\n\nAverage air relative humidity radaytion for the 6 days following the given date.\n\n\n\n\n\nwind_[N|NE|E|SE|S|SW|W|NW] *_days_after  [1,13]\n\n\n\nWind index for the North, North-East, East, South-East, South, South-West, West, North-West area for the 13 days before the given date.\n\n\n\n\n\nind_[N|NE|E|SE|S|SW|W|NW] *_days_after [1,6]\n\n\n\nWind index for the North, North-East, East, South-East, South, South-West, West, North-West area for the 6 days following the given date.\n\n\n\n\n\ngdd_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum\n\n\n\nGDD heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum).\n\n\n\n\n\ngdd_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum *_weeks_before [1|2]\n\n\n\nGDD heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the 2 weeks previous to the given day.\n\n\n\n\n\ngdd_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum * 1_weeks_after\n\n\n\nGDD heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the next week to the given day.\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_[TBase|Tbasemin]_sum\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum).\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_[TBase|Tbasemin]_sum *_weeks_before [1|2]\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the 2 weeks previous to the given day.\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_[TBase|Tbasemin]_sum * 1_weeks_after\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the next week to the given day.\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_ Utah _sum\n\n\n\nUtah cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum).\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_ Utah _sum *_weeks_before [1|2]\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the 2 weeks previous to the given day.\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_ Utah _sum * 1_weeks_after\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the next week to the given day.\n\n\n\n\n\nrad_sum\n\n\n\nAccumulated radaytion since the beginning of the season until the given date.\n\n\n\n\n\nrad_sum *_weeks_before [1|2]\n\n\n\nAccumulated radaytion since the beginning of the season until 1 or 2 weeks before the given date.\n\n\n\n\n\nrad_sum 1_weeks_after\n\n\n\nAccumulated radaytion since the beginning of the season until the next week after the given date.\n\n\n\n\n\nprecip_sum\n\n\n\nAccumulated precipitation since the beginning of the season until the given date.\n\n\n\n\n\nprecip_sum *_weeks_before [1|2]\n\n\n\nAccumulated precipitation since the beginning of the season until 1 or 2 weeks before the given date.\n\n\n\n\n\nprecip_sum 1_weeks_after\n\n\n\nAccumulated precipitation since the beginning of the season until the next week after the given date.\n\n\n\n\n\nwinkler_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum\n\n\n\nWinkler heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum).\n\n\n\n\n\nwinkler_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum *_weeks_before [1|2]\n\n\n\nWinkler heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the 2 weeks previous to the given day.\n\n\n\n\n\nwinkler_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum * 1_weeks_after\n\n\n\nWinkler heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the next week after the given day.\n\n\n\n\n\n \n\n \n\nThe GDD, Winkler and Chilling (Richardson and Utah) indexes are also calculated considering the contributions of the time units (periods) to the daily contribution. These fields (or columns of the file) have the same naming schema as their counterparts based on daily calculations but with the “cumm” suffix.\n\nFile “DIF phenologicalstages.csv” Description\n\nThis file contains a description of the different types of phenological stages considered. The fields are:\n\n\n\nitainnovaid: this is an identifier of the phenological stage.\n\nbbch: the number of stage in the BBCH phenological stage.\n\nDescripción BBCH: this is a textual description of the previous BBCH phenological stage.\n\n\nThe contents of the file are as follows:\n\n \n\n\n\n\n\n\nitainnovaid\n\n\n\nbbch\n\n\n\nDescripción BBCH\n\n\n\n\n\n0\n\n\n\n0\n\n\n\nWinter dormancy or resting period\n\n\n\n\n\n3\n\n\n\n63\n\n\n\nEarly flowering: 30% of flowerhoods fallen\n\n\n\n\n\n1\n\n\n\n11\n\n\n\nFirst leaf unfolded and spread away from shoot\n\n\n\n\n\n2\n\n\n\n15\n\n\n\n5 leaves unfolded\n\n\n\n\n\n4\n\n\n\n65\n\n\n\nFull flowering: 50% of flowerhoods fallen\n\n\n\n\n\n6\n\n\n\n71\n\n\n\nFruit set: young fruits begin to swell, remains of flowers\n\n\n\n\n\n5\n\n\n\n68\n\n\n\n80% of flowerhoods fallen\n\n\n\n\n\n7\n\n\n\n75\n\n\n\n50% of fruits have reached final size or fruit has reached 50% of final size\n\n\n\n\n\n8\n\n\n\n77\n\n\n\n70% of fruits have reached final size or fruit has reached 70% of final size\n\n\n\n\n\n9\n\n\n\n81\n\n\n\nBeginning of ripening or fruit colouration","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"awardTitle":"NEAT-AMBIENCE - Next-gEnerATion dAta Management to foster suitable Behaviors and the resilience of cItizens against modErN ChallEnges","funderName":"Agencia Estatal de Investigación","awardNumber":"PID2020-113037RB-I00","funderIdentifier":"10.13039/501100011033","funderIdentifierType":"Crossref Funder ID"},{"awardTitle":"COSMOS, Computer Science for Complex System modelling","funderName":"Gobierno de Aragón","awardNumber":"T64_23R","funderIdentifier":"10.13039/501100010067","funderIdentifierType":"Crossref Funder ID"}],"url":"https://zenodo.org/doi/10.5281/zenodo.17930723","contentUrl":null,"metadataVersion":13,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-04-08T17:59:11Z","registered":"2026-04-08T17:59:11Z","published":null,"updated":"2026-05-09T17:34:01Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.20098784","type":"dois","attributes":{"doi":"10.5281/zenodo.20098784","identifiers":[],"creators":[{"name":"Leizerman, Samuel","nameType":"Personal","givenName":"Samuel","familyName":"Leizerman","nameIdentifiers":[{"nameIdentifier":"0009-0000-0133-2291","nameIdentifierScheme":"ORCID"}],"affiliation":[]}],"titles":[{"title":"It Is Bit: Unit-Testing HC Tokens Non-Markovian Causal Memory For Emergence of Lorentzian Metric, Non-Associativity, Zero-Divisor Dissolution and Other Primitives for Recovering Coord State Spacetime from AI Info Field"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Information Theory","subjectScheme":"MeSH"},{"subject":"Statistical information","subjectScheme":"GEMET"},{"subject":"Artificial intelligence","subjectScheme":"EuroSciVoc"},{"subject":"Artificial Intelligence/standards","subjectScheme":"MeSH"},{"subject":"Condensed matter physics","subjectScheme":"EuroSciVoc"},{"subject":"Particle physics","subjectScheme":"EuroSciVoc"},{"subject":"Physical cosmology","subjectScheme":"EuroSciVoc"},{"subject":"Quantum physics","subjectScheme":"EuroSciVoc"},{"subject":"Physics","subjectScheme":"GEMET"},{"subject":"Physics","subjectScheme":"MeSH"},{"subject":"Mathematical physics","subjectScheme":"EuroSciVoc"},{"subject":"Transport (physics)","subjectScheme":"GEMET"},{"subject":"Theoretical physics","subjectScheme":"EuroSciVoc"},{"subject":"Statistics, Nonparametric","subjectScheme":"MeSH"},{"subject":"Statistical mechanics","subjectScheme":"EuroSciVoc"}],"contributors":[{"name":"Leizerman, Samuel","nameType":"Personal","givenName":"Samuel","familyName":"Leizerman","contributorType":"Researcher","nameIdentifiers":[{"nameIdentifier":"0009-0000-0133-2291","nameIdentifierScheme":"ORCID"}],"affiliation":[]}],"dates":[{"date":"2026-05-09","dateType":"Issued"},{"date":"2026-05-09","dateType":"Created"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.20098784","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution Non Commercial 4.0 International","rightsUri":"https://creativecommons.org/licenses/by-nc/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-nc-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The biquaternion algebra ℍ_ℂ = ℂ ⊗_ℝ ℍ ≅ M₂(ℂ) is associative and contains zero divisors. Equipped with a causal memory kernel and differential parallel transport, it recovers effective non-associativity, zero-divisor dissolution, and Lorentzian spacetime.\n\nConstruction. Tokens are embedded as biquaternions via the equation-of-state form qₙ = Aₙe^{aₙ} + Bₙe^{ibₙ}. Each interaction Pₓ · Pⱼ produces four complex components c_μ, contributing to the rank-4 informational stress-energy tensor as the Landauer-weighted outer product\n\nT̂^{μν}_{ρσ}(x) = (k_B T(x) ln 2 / V_cell(x)) Σᵢ₌₁^{N(x)} p̂^{μν}_{(i)} ⊗ p̂_{(i)ρσ},     p̂^{μν}_{(i)} = c_μ c̄_ν.\n\nThe Landauer prefactor k_B T(x) ln 2 / V_cell(x) is the energy density per erased bit at local informational temperature. The effective metric is extracted by double contraction g^{μν} = Σ_ρ T^{μρν}_ρ, symmetrized.\n\nSignature test. The induced g^{μν} is decomposed into Hermitian and anti-Hermitian projections under the biquaternionic conjugate q† = q̄₀ − q̄₁i − q̄₂j − q̄₃k:\n\nℋ(q) = ½(q + q†),     𝒜(q) = ½(q − q†).\n\nEigenvalue signs at each test point give the metric signature.\n\nResults (N = 6 token positions, all positions tested):\n\n  Projection      Signature   Hit rate  ℋ(g^{μν})       (1,3)       6/6  𝒜(g^{μν})       (3,1)       6/6  Full g^{μν}     (2,2)       6/6\n\nThe Hermitian projection yields Lorentzian signature, the anti-Hermitian yields the time-reversed Lorentzian, the full algebra sits on the neutral (2,2) signature characteristic of ℍ_ℂ as a real 4D algebra. Lorentzian signature is recovered by projection rather than postulated. Energy partition ‖ℋ‖² / ‖𝒜‖² ≈ 0.5 across all 15 pairwise interactions.\n\nA variable-order entropy functional S_{q(x)}[ρ] on S² breaks the KL-versus-resolution tradeoff and produces directional attention asymmetry. Fed biquaternionic source currents from the Hermitian-energy field E(x) = Σ_{y≠x} ‖ℋ(PₓP_y)‖², it develops a backward-looking bias Pearson-correlated at −0.86 ± 0.03 with E(x) across N = 10 to N = 2000, with bias decaying as N^{−0.42} matching the kernel exponent α_K = 0.5. Coupling is invariant under structured-vs-unstructured token controls.\n\nThe dynamical weight W(α) = Z_p + e^{iπα/2} Z_q decomposes into three sectors under U(1) rotation: geometric (period ∞), mixed (period 4), spectral (period 2). Under the Spin(5) unit, these map to its three smallest irreps: 𝟏, 𝟒, 𝟓. The biquaternionic U(1) closure is the rung-2 instance of a tower-wide closure on G₂ ⊂ F₄ ⊂ E₆ ⊂ E₇ ⊂ E₈ ⊂ E₈ × E₈ ↪ Cl(9,1) with winding pattern (1, 2, 4, 4, 4, 1) at the static level.\n\nThe closure equation has a structural reading that sharpens Wheeler's \"it from bit\" thesis (Wheeler, \"Information, Physics, Quantum: The Search for Links,\" 1990). The framework's lattice projection does not produce physical structure from bit-like substrate; the terminal closure of the framework's lattice projection is bit-like substrate, with the static closure dimension at 2⁹, the cap at 2⁴, the chain's spinor doublings at 2⁵, 2⁶, 2⁷, and the dynamic closure at 2¹⁰. It is not from bit. It is bit. The Cayley-Dickson tower and the magic-square exceptional chain forced by Hurwitz, Cartan, and Spin(9) cap minimality together produce a closure hierarchy in powers of 2, and the powers of 2 are not a substrate the physics reduces to but the structural arithmetic the closure operation itself satisfies.\n\nThe eight equivalence classes of the lattice projection sort into three observable-regime types corresponding to the three pieces of the cumulative winding 7 + 8 + 1 = 16: cap-anchored observables (electron mass at the actualization scale), trajectory observables (α_EM(μ) running across the bifurcation), and coherent-magnitude observables (ρ_Λ as the integrated coherent-winding fraction). These three regime types are the structural grammar in which the eight taxonomic groups are written.\n\nSections 1–14 establish the biquaternionic construction and signature, entropy, and dynamical weight results. Sections 16–18 develop the tower-wide closure and the static-residual cascade verified at the rung-2/rung-3 boundary, with v9.3 §17.8 extending the static Cl(9) closure to the dynamic Cl(9,1) ambient that supports memory and chirality through Majorana–Weyl reality conditions.\n\nAll computations are explicit, reproducible, and use only NumPy and SciPy.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.20098784","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-05-09T17:27:59Z","registered":"2026-05-09T17:27:59Z","published":null,"updated":"2026-05-09T17:30:05Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.20099323","type":"dois","attributes":{"doi":"10.5281/zenodo.20099323","identifiers":[{"identifier":"oai:zenodo.org:20099323","identifierType":"oai"}],"creators":[{"name":"Leizerman, Samuel","nameType":"Personal","givenName":"Samuel","familyName":"Leizerman","nameIdentifiers":[{"nameIdentifier":"0009-0000-0133-2291","nameIdentifierScheme":"ORCID"}],"affiliation":[]}],"titles":[{"title":"It Is Bit: Unit-Testing HC Tokens Non-Markovian Causal Memory For Emergence of Lorentzian Metric, Non-Associativity, Zero-Divisor Dissolution and Other Primitives for Recovering Coord State Spacetime from AI Info Field"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Information Theory","subjectScheme":"MeSH"},{"subject":"Statistical information","subjectScheme":"GEMET"},{"subject":"Artificial intelligence","subjectScheme":"EuroSciVoc"},{"subject":"Artificial Intelligence/standards","subjectScheme":"MeSH"},{"subject":"Condensed matter physics","subjectScheme":"EuroSciVoc"},{"subject":"Particle physics","subjectScheme":"EuroSciVoc"},{"subject":"Physical cosmology","subjectScheme":"EuroSciVoc"},{"subject":"Quantum physics","subjectScheme":"EuroSciVoc"},{"subject":"Physics","subjectScheme":"GEMET"},{"subject":"Physics","subjectScheme":"MeSH"},{"subject":"Mathematical physics","subjectScheme":"EuroSciVoc"},{"subject":"Transport (physics)","subjectScheme":"GEMET"},{"subject":"Theoretical physics","subjectScheme":"EuroSciVoc"},{"subject":"Statistics, Nonparametric","subjectScheme":"MeSH"},{"subject":"Statistical mechanics","subjectScheme":"EuroSciVoc"}],"contributors":[{"name":"Leizerman, Samuel","nameType":"Personal","givenName":"Samuel","familyName":"Leizerman","contributorType":"Researcher","nameIdentifiers":[{"nameIdentifier":"0009-0000-0133-2291","nameIdentifierScheme":"ORCID"}],"affiliation":[]}],"dates":[{"date":"2026-05-09","dateType":"Issued"},{"date":"2026-05-09","dateType":"Created"}],"language":null,"types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.20098784","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution Non Commercial 4.0 International","rightsUri":"https://creativecommons.org/licenses/by-nc/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-nc-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The biquaternion algebra ℍ_ℂ = ℂ ⊗_ℝ ℍ ≅ M₂(ℂ) is associative and contains zero divisors. Equipped with a causal memory kernel and differential parallel transport, it recovers effective non-associativity, zero-divisor dissolution, and Lorentzian spacetime.\n\nConstruction. Tokens are embedded as biquaternions via the equation-of-state form qₙ = Aₙe^{aₙ} + Bₙe^{ibₙ}. Each interaction Pₓ · Pⱼ produces four complex components c_μ, contributing to the rank-4 informational stress-energy tensor as the Landauer-weighted outer product\n\nT̂^{μν}_{ρσ}(x) = (k_B T(x) ln 2 / V_cell(x)) Σᵢ₌₁^{N(x)} p̂^{μν}_{(i)} ⊗ p̂_{(i)ρσ},     p̂^{μν}_{(i)} = c_μ c̄_ν.\n\nThe Landauer prefactor k_B T(x) ln 2 / V_cell(x) is the energy density per erased bit at local informational temperature. The effective metric is extracted by double contraction g^{μν} = Σ_ρ T^{μρν}_ρ, symmetrized.\n\nSignature test. The induced g^{μν} is decomposed into Hermitian and anti-Hermitian projections under the biquaternionic conjugate q† = q̄₀ − q̄₁i − q̄₂j − q̄₃k:\n\nℋ(q) = ½(q + q†),     𝒜(q) = ½(q − q†).\n\nEigenvalue signs at each test point give the metric signature.\n\nResults (N = 6 token positions, all positions tested):\n\n  Projection      Signature   Hit rate  ℋ(g^{μν})       (1,3)       6/6  𝒜(g^{μν})       (3,1)       6/6  Full g^{μν}     (2,2)       6/6\n\nThe Hermitian projection yields Lorentzian signature, the anti-Hermitian yields the time-reversed Lorentzian, the full algebra sits on the neutral (2,2) signature characteristic of ℍ_ℂ as a real 4D algebra. Lorentzian signature is recovered by projection rather than postulated. Energy partition ‖ℋ‖² / ‖𝒜‖² ≈ 0.5 across all 15 pairwise interactions.\n\nA variable-order entropy functional S_{q(x)}[ρ] on S² breaks the KL-versus-resolution tradeoff and produces directional attention asymmetry. Fed biquaternionic source currents from the Hermitian-energy field E(x) = Σ_{y≠x} ‖ℋ(PₓP_y)‖², it develops a backward-looking bias Pearson-correlated at −0.86 ± 0.03 with E(x) across N = 10 to N = 2000, with bias decaying as N^{−0.42} matching the kernel exponent α_K = 0.5. Coupling is invariant under structured-vs-unstructured token controls.\n\nThe dynamical weight W(α) = Z_p + e^{iπα/2} Z_q decomposes into three sectors under U(1) rotation: geometric (period ∞), mixed (period 4), spectral (period 2). Under the Spin(5) unit, these map to its three smallest irreps: 𝟏, 𝟒, 𝟓. The biquaternionic U(1) closure is the rung-2 instance of a tower-wide closure on G₂ ⊂ F₄ ⊂ E₆ ⊂ E₇ ⊂ E₈ ⊂ E₈ × E₈ ↪ Cl(9,1) with winding pattern (1, 2, 4, 4, 4, 1) at the static level.\n\nThe closure equation has a structural reading that sharpens Wheeler's \"it from bit\" thesis (Wheeler, \"Information, Physics, Quantum: The Search for Links,\" 1990). The framework's lattice projection does not produce physical structure from bit-like substrate; the terminal closure of the framework's lattice projection is bit-like substrate, with the static closure dimension at 2⁹, the cap at 2⁴, the chain's spinor doublings at 2⁵, 2⁶, 2⁷, and the dynamic closure at 2¹⁰. It is not from bit. It is bit. The Cayley-Dickson tower and the magic-square exceptional chain forced by Hurwitz, Cartan, and Spin(9) cap minimality together produce a closure hierarchy in powers of 2, and the powers of 2 are not a substrate the physics reduces to but the structural arithmetic the closure operation itself satisfies.\n\nThe eight equivalence classes of the lattice projection sort into three observable-regime types corresponding to the three pieces of the cumulative winding 7 + 8 + 1 = 16: cap-anchored observables (electron mass at the actualization scale), trajectory observables (α_EM(μ) running across the bifurcation), and coherent-magnitude observables (ρ_Λ as the integrated coherent-winding fraction). These three regime types are the structural grammar in which the eight taxonomic groups are written.\n\nSections 1–14 establish the biquaternionic construction and signature, entropy, and dynamical weight results. Sections 16–18 develop the tower-wide closure and the static-residual cascade verified at the rung-2/rung-3 boundary, with v9.3 §17.8 extending the static Cl(9) closure to the dynamic Cl(9,1) ambient that supports memory and chirality through Majorana–Weyl reality conditions.\n\nAll computations are explicit, reproducible, and use only NumPy and SciPy.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.20099323","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-05-09T17:29:09Z","registered":"2026-05-09T17:29:09Z","published":null,"updated":"2026-05-09T17:29:09Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.20038410","type":"dois","attributes":{"doi":"10.5281/zenodo.20038410","identifiers":[],"creators":[{"name":"Miotello, Federico","nameType":"Personal","givenName":"Federico","familyName":"Miotello","affiliation":["Politecnico di Milano"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-2130-6423","nameIdentifierScheme":"ORCID"}]},{"name":"Ostan, Paolo","nameType":"Personal","givenName":"Paolo","familyName":"Ostan","nameIdentifiers":[{"nameIdentifier":"0009-0005-6366-7560","nameIdentifierScheme":"ORCID"}],"affiliation":[]},{"name":"Del Gaudio, Francesca","nameType":"Personal","givenName":"Francesca","familyName":"Del Gaudio","nameIdentifiers":[{"nameIdentifier":"0000-0002-2647-5907","nameIdentifierScheme":"ORCID"}],"affiliation":[]},{"name":"Comanducci, Luca","nameType":"Personal","givenName":"Luca","familyName":"Comanducci","affiliation":["Politecnico di Milano"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-4167-5173","nameIdentifierScheme":"ORCID"}]},{"name":"Malvermi, Raffaele","nameType":"Personal","givenName":"Raffaele","familyName":"Malvermi","nameIdentifiers":[{"nameIdentifier":"0000-0002-5586-6376","nameIdentifierScheme":"ORCID"}],"affiliation":[]},{"name":"Pezzoli, Mirco","nameType":"Personal","givenName":"Mirco","familyName":"Pezzoli","affiliation":["Politecnico di Milano"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-1296-0992","nameIdentifierScheme":"ORCID"}]},{"name":"Antonacci, Fabio","nameType":"Personal","givenName":"Fabio","familyName":"Antonacci","affiliation":["Politecnico di Milano"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-4545-0315","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"THE SOUND OF THE VIOLIN'S HOME: A HIGHER-ORDER ROOM IMPULSE RESPONSE DATASET OF THE ARVEDI AUDITORIUM IN CREMONA"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Acoustics","subjectScheme":"EuroSciVoc"},{"subject":"Acoustics","subjectScheme":"MeSH"},{"subject":"Acoustics","subjectScheme":"GEMET"},{"subject":"Sound transmission","subjectScheme":"GEMET"},{"subject":"Sound Recordings","subjectScheme":"MeSH"},{"subject":"Sound Localization","subjectScheme":"MeSH"},{"subject":"Sound measurement","subjectScheme":"GEMET"},{"subject":"Sound propagation","subjectScheme":"GEMET"}],"contributors":[],"dates":[{"date":"2026-05-05","dateType":"Issued"},{"date":"2026","dateType":"Created","dateInformation":"Acoustics"}],"language":"en","types":{"ris":"MPCT","bibtex":"misc","citeproc":"article","schemaOrg":"MediaObject","resourceType":"","resourceTypeGeneral":"Audiovisual"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.20038410","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.20038410","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":2,"versionOfCount":1,"created":"2026-05-06T08:38:39Z","registered":"2026-05-06T08:38:39Z","published":null,"updated":"2026-05-09T17:27:59Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19758622","type":"dois","attributes":{"doi":"10.5281/zenodo.19758622","identifiers":[],"creators":[{"name":"Hami, Ibriyamov","nameType":"Personal","givenName":"Ibriyamov","familyName":"Hami","nameIdentifiers":[{"nameIdentifier":"0009-0007-9390-113X","nameIdentifierScheme":"ORCID"}],"affiliation":[]}],"titles":[{"title":"The HPA axis's role in chronic stress and neuropsychiatric disorders. Pathways to malignant formations and autoimmune disorders through chronic stress"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Hypothalamic-Pituitary-Gonadal Axis","subjectScheme":"MeSH"},{"subject":"Autoimmune diseases","subjectScheme":"EuroSciVoc"},{"subject":"Neuropsychiatric disorders"}],"contributors":[],"dates":[{"date":"2026-05-08","dateType":"Issued"},{"date":"2026-05-08","dateType":"Updated"}],"language":"en","types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"Other","resourceTypeGeneral":"Text"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19758622","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"v1.2","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Chronic stress is not merely a psychological state — it is a physiological cascade with measurable, compounding consequences across the endocrine, immune, and nervous systems. This ongoing literature synthesis maps the hypothalamic-pituitary-adrenal (HPA) axis and its dysregulation under chronic stress, integrating neuroendocrinology, immunology, and psychiatric research into a single mechanistic account. Coverage includes the CRH–ACTH–glucocorticoid pipeline, AVP and POMC contributions, glucocorticoid receptor signaling, endocannabinoid-mediated fast feedback at PVN CRH neurons, hippocampal and prefrontal regulation of stress termination, amygdalar excitatory drive, and the locus coeruleus–noradrenergic system. Modulators of the stress response (gonadal steroids, developmental stage, stressor type) are also examined. Three downstream pathologies are traced in detail: thymic atrophy via glucocorticoid-induced DP thymocyte apoptosis — linking chronic stress to autoimmune disorders and impaired antitumor surveillance — hippocampal damage from sustained hypercortisolism, and HPA hyperactivity in depression and PTSD. Work in progress; the closing section on glucocorticoid-induced hippocampal damage in neuropsychiatric disorders is forthcoming.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.19758622","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":3,"versionOfCount":1,"created":"2026-04-25T09:37:53Z","registered":"2026-04-25T09:37:53Z","published":null,"updated":"2026-05-09T17:20:17Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.20080935","type":"dois","attributes":{"doi":"10.5281/zenodo.20080935","identifiers":[{"identifier":"oai:zenodo.org:20080935","identifierType":"oai"}],"creators":[{"name":"Hami, Ibriyamov","nameType":"Personal","givenName":"Ibriyamov","familyName":"Hami","nameIdentifiers":[{"nameIdentifier":"0009-0007-9390-113X","nameIdentifierScheme":"ORCID"}],"affiliation":[]}],"titles":[{"title":"The HPA axis's role in chronic stress and neuropsychiatric disorders. Pathways to malignant formations and autoimmune disorders through chronic stress"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Hypothalamic-Pituitary-Gonadal Axis","subjectScheme":"MeSH"},{"subject":"Autoimmune diseases","subjectScheme":"EuroSciVoc"},{"subject":"Neuropsychiatric disorders"}],"contributors":[],"dates":[{"date":"2026-05-08","dateType":"Issued"},{"date":"2026-05-08","dateType":"Updated"}],"language":"en","types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"Other","resourceTypeGeneral":"Text"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19758622","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"v1.2","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Chronic stress is not merely a psychological state — it is a physiological cascade with measurable, compounding consequences across the endocrine, immune, and nervous systems. This ongoing literature synthesis maps the hypothalamic-pituitary-adrenal (HPA) axis and its dysregulation under chronic stress, integrating neuroendocrinology, immunology, and psychiatric research into a single mechanistic account. Coverage includes the CRH–ACTH–glucocorticoid pipeline, AVP and POMC contributions, glucocorticoid receptor signaling, endocannabinoid-mediated fast feedback at PVN CRH neurons, hippocampal and prefrontal regulation of stress termination, amygdalar excitatory drive, and the locus coeruleus–noradrenergic system. Modulators of the stress response (gonadal steroids, developmental stage, stressor type) are also examined. Three downstream pathologies are traced in detail: thymic atrophy via glucocorticoid-induced DP thymocyte apoptosis — linking chronic stress to autoimmune disorders and impaired antitumor surveillance — hippocampal damage from sustained hypercortisolism, and HPA hyperactivity in depression and PTSD. Work in progress; the closing section on glucocorticoid-induced hippocampal damage in neuropsychiatric disorders is forthcoming.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.20080935","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-05-08T08:27:46Z","registered":"2026-05-08T08:27:46Z","published":null,"updated":"2026-05-09T17:20:17Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.20094830","type":"dois","attributes":{"doi":"10.5281/zenodo.20094830","identifiers":[{"identifier":"oai:zenodo.org:20094830","identifierType":"oai"}],"creators":[{"name":"Willem Jan Schotsman, Willem Jan","nameType":"Personal","givenName":"Willem Jan","familyName":"Willem Jan Schotsman","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"Emergent Standard Model Parameters from $E_8$ Quasicrystal Torsion and $SO(10)$ Quantization Noise"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Particle physics","subjectScheme":"EuroSciVoc"},{"subject":"Quantum physics","subjectScheme":"EuroSciVoc"},{"subject":"relativity"},{"subject":"gravity"},{"subject":"Information Theory","subjectScheme":"MeSH"},{"subject":"standard model"}],"contributors":[],"dates":[{"date":"2026-05-09","dateType":"Issued"}],"language":null,"types":{"ris":"RPRT","bibtex":"article","citeproc":"article-journal","schemaOrg":"ScholarlyArticle","resourceType":"","resourceTypeGeneral":"Text"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.20091530","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"2.0","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"},{"rights":"Willem Jan Schotsman 2026","rightsUri":"http://rightsstatements.org/vocab/InC/1.0/"}],"descriptions":[{"description":"Title: The Beable Engine V11.5.0: Algorithmic Superdeterminism and the $E_8$ Quasicrystal Manifold\n\nDescription:\n\nOverview\n\nThis repository contains the theoretical manuscripts, telemetry data, and the C# source code for the Beable Engine (V11.5.0). The Beable Engine proposes a Beyond Standard Model (BSM) geometric framework operating on the principles of Algorithmic Superdeterminism. It abandons the orthodox Quantum Field Theory (QFT) assumption of a continuous Lorentzian manifold, instead modeling the vacuum as a discrete, 1.5-bit ternary computational graph projected from an 8-dimensional $E_8$ Lie group.\n\nBy utilizing a highly constrained set of axiomatic geometric seeds—specifically the Golden Ratio ($\\Phi$), the Hausdorff fractal dimension ($D \\approx 2.32$), and Topological Torsion ($\\tau$)—the engine acts as a sophisticated data-compression algorithm. It mathematically reverse-engineers the empirical parameters of modern physics into pure algebraic geometry, deriving Standard Model constants without the need for manual curve-fitting.\n\nRepository Contents\n\n\n\n\n\nProgram.cs (Beable Engine V11.5.0): The core computational engine. It strictly demarcates the \"SM Gauge Sector\" (frozen topological invariants) from the \"BSM Hypothesis Space\" (dynamically evolving parameters subject to topological friction). It calculates 26 SM parameters, including the fine-structure constant ($\\alpha^{-1} \\approx 137.035$) and proton-to-electron mass ratio ($\\mu \\approx 1836.157$).\n\n\n\n\noutput.txt: Telemetry logs demonstrating the \"Archetype\" ($t=0.001$), \"Current\" ($t=22.308$), and \"Omega\" ($t=248$) epoch outputs, highlighting the topological unwinding of the parameters.\n\n\n\n\nTheoretical Manuscripts: A collection of six interconnected papers that lay the epistemological and mathematical groundwork for the engine:\n\n\n\n\n\nEmergent Standard Model Parameters from $E_8$ Quasicrystal Torsion and $SO(10)$ Quantization Noise\n\n\n\n\nEmergent Standard Model Parameters from $E_8$ Quasicrystal Topology and Algorithmic Unwinding\n\n\n\n\nEmpirical Convergence of a Zero-Parameter $E_8$ Quasicrystal Manifold\n\n\n\n\nTopological Unwinding of the Flavor Puzzle\n\n\n\n\nMajoranaDetect: Side-Channel Attacks on the $E_8$ Manifold\n\n\n\n\nThe Unreasonable Effectiveness of Geometric Curve-Fitting\n\n\n\n\n\nKey Theoretical Highlights\n\n\n\n\n\nThe Flavor Puzzle \u0026 Fractal Drag: The engine addresses the Mass Hierarchy and the Problem of Generations natively. Mass is redefined as \"Fractal Drag\"—the topological friction encountered by knots moving through the ternary lattice. The existence of exactly three generations is dictated by standing-wave topological saturation limits ($3^1, 3^2, 3^3$) on the $D \\approx 2.32$ manifold.\n\n\n\n\nCosmological Unwinding: Time ($t$) is modeled as a \"Topological Unwinding Coordinate.\" Dark Energy ($\\Omega_\\Lambda \\approx 67.85\\%$), Dark Matter ($\\Omega_{DM} \\approx 27.23\\%$), and Baryonic Matter ($\\Omega_{BM} \\approx 4.92\\%$) emerge dynamically from the expansion of the computational graph.\n\n\n\n\nThe Majorana Anchor: The neutrino mass hierarchy is resolved via an explicitly derived Right-Handed Majorana anchor ($M_{Seesaw} \\approx 2.11 \\times 10^{14}$ GeV), generated from the Planck Mass and the $E_8$ roots.\n\n\n\nFalsifiable Predictions\n\nThe framework moves beyond post-diction by offering strict, falsifiable targets for the global experimental physics community:\n\n\n\n\n\nDUNE Experiment: The model predicts the Leptonic CP-violating phase ($\\delta_{CP}$) to be exactly the circular holonomy of the Golden Ratio ($\\delta_{CP} = \\pi \\Phi \\approx 291.24^\\circ$).\n\n\n\n\nLHC Signatures: The model predicts a specific \"memory leak\" into the higher 8th dimension during Top Quark creation, generating anomalous decay missing-energy signatures that scale perfectly by the topological torsion variable ($\\tau = 2/\\Phi$).","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.20094830","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-05-09T09:22:59Z","registered":"2026-05-09T09:22:59Z","published":null,"updated":"2026-05-09T17:20:07Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.20091530","type":"dois","attributes":{"doi":"10.5281/zenodo.20091530","identifiers":[],"creators":[{"name":"Willem Jan Schotsman, Willem Jan","nameType":"Personal","givenName":"Willem Jan","familyName":"Willem Jan Schotsman","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"Emergent Standard Model Parameters from $E_8$ Quasicrystal Torsion and $SO(10)$ Quantization Noise"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Particle physics","subjectScheme":"EuroSciVoc"},{"subject":"Quantum physics","subjectScheme":"EuroSciVoc"},{"subject":"relativity"},{"subject":"gravity"},{"subject":"Information Theory","subjectScheme":"MeSH"},{"subject":"standard model"}],"contributors":[],"dates":[{"date":"2026-05-09","dateType":"Issued"}],"language":null,"types":{"ris":"RPRT","bibtex":"article","citeproc":"article-journal","schemaOrg":"ScholarlyArticle","resourceType":"","resourceTypeGeneral":"Text"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.20091530","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"2.0","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"},{"rights":"Willem Jan Schotsman 2026","rightsUri":"http://rightsstatements.org/vocab/InC/1.0/"}],"descriptions":[{"description":"Title: The Beable Engine V11.5.0: Algorithmic Superdeterminism and the $E_8$ Quasicrystal Manifold\n\nDescription:\n\nOverview\n\nThis repository contains the theoretical manuscripts, telemetry data, and the C# source code for the Beable Engine (V11.5.0). The Beable Engine proposes a Beyond Standard Model (BSM) geometric framework operating on the principles of Algorithmic Superdeterminism. It abandons the orthodox Quantum Field Theory (QFT) assumption of a continuous Lorentzian manifold, instead modeling the vacuum as a discrete, 1.5-bit ternary computational graph projected from an 8-dimensional $E_8$ Lie group.\n\nBy utilizing a highly constrained set of axiomatic geometric seeds—specifically the Golden Ratio ($\\Phi$), the Hausdorff fractal dimension ($D \\approx 2.32$), and Topological Torsion ($\\tau$)—the engine acts as a sophisticated data-compression algorithm. It mathematically reverse-engineers the empirical parameters of modern physics into pure algebraic geometry, deriving Standard Model constants without the need for manual curve-fitting.\n\nRepository Contents\n\n\n\n\n\nProgram.cs (Beable Engine V11.5.0): The core computational engine. It strictly demarcates the \"SM Gauge Sector\" (frozen topological invariants) from the \"BSM Hypothesis Space\" (dynamically evolving parameters subject to topological friction). It calculates 26 SM parameters, including the fine-structure constant ($\\alpha^{-1} \\approx 137.035$) and proton-to-electron mass ratio ($\\mu \\approx 1836.157$).\n\n\n\n\noutput.txt: Telemetry logs demonstrating the \"Archetype\" ($t=0.001$), \"Current\" ($t=22.308$), and \"Omega\" ($t=248$) epoch outputs, highlighting the topological unwinding of the parameters.\n\n\n\n\nTheoretical Manuscripts: A collection of six interconnected papers that lay the epistemological and mathematical groundwork for the engine:\n\n\n\n\n\nEmergent Standard Model Parameters from $E_8$ Quasicrystal Torsion and $SO(10)$ Quantization Noise\n\n\n\n\nEmergent Standard Model Parameters from $E_8$ Quasicrystal Topology and Algorithmic Unwinding\n\n\n\n\nEmpirical Convergence of a Zero-Parameter $E_8$ Quasicrystal Manifold\n\n\n\n\nTopological Unwinding of the Flavor Puzzle\n\n\n\n\nMajoranaDetect: Side-Channel Attacks on the $E_8$ Manifold\n\n\n\n\nThe Unreasonable Effectiveness of Geometric Curve-Fitting\n\n\n\n\n\nKey Theoretical Highlights\n\n\n\n\n\nThe Flavor Puzzle \u0026 Fractal Drag: The engine addresses the Mass Hierarchy and the Problem of Generations natively. Mass is redefined as \"Fractal Drag\"—the topological friction encountered by knots moving through the ternary lattice. The existence of exactly three generations is dictated by standing-wave topological saturation limits ($3^1, 3^2, 3^3$) on the $D \\approx 2.32$ manifold.\n\n\n\n\nCosmological Unwinding: Time ($t$) is modeled as a \"Topological Unwinding Coordinate.\" Dark Energy ($\\Omega_\\Lambda \\approx 67.85\\%$), Dark Matter ($\\Omega_{DM} \\approx 27.23\\%$), and Baryonic Matter ($\\Omega_{BM} \\approx 4.92\\%$) emerge dynamically from the expansion of the computational graph.\n\n\n\n\nThe Majorana Anchor: The neutrino mass hierarchy is resolved via an explicitly derived Right-Handed Majorana anchor ($M_{Seesaw} \\approx 2.11 \\times 10^{14}$ GeV), generated from the Planck Mass and the $E_8$ roots.\n\n\n\nFalsifiable Predictions\n\nThe framework moves beyond post-diction by offering strict, falsifiable targets for the global experimental physics community:\n\n\n\n\n\nDUNE Experiment: The model predicts the Leptonic CP-violating phase ($\\delta_{CP}$) to be exactly the circular holonomy of the Golden Ratio ($\\delta_{CP} = \\pi \\Phi \\approx 291.24^\\circ$).\n\n\n\n\nLHC Signatures: The model predicts a specific \"memory leak\" into the higher 8th dimension during Top Quark creation, generating anomalous decay missing-energy signatures that scale perfectly by the topological torsion variable ($\\tau = 2/\\Phi$).","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.20091530","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-05-09T00:48:07Z","registered":"2026-05-09T00:48:07Z","published":null,"updated":"2026-05-09T17:20:07Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.20099090","type":"dois","attributes":{"doi":"10.5281/zenodo.20099090","identifiers":[{"identifier":"oai:zenodo.org:20099090","identifierType":"oai"}],"creators":[{"name":"DOTCHE, Ameyo Mireille","nameType":"Personal","givenName":"Ameyo Mireille","familyName":"DOTCHE","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"Inscrire un site archéologique sous-marin sur la Liste du patrimoine mondial de l'UNESCO : enjeux méthodologiques, conditions de protection et intégration de la gestion des risques. Réflexions à partir du cas du Phare d'Alexandrie"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Risk Management","subjectScheme":"MeSH"},{"subject":"Underwater archaeology","subjectScheme":"EuroSciVoc"},{"subject":"UNESCO","subjectScheme":"MeSH"},{"subject":"lighthouse of alexandria"},{"subject":"tentative list"},{"subject":"universal value"},{"subject":"conservation"}],"contributors":[],"dates":[{"date":"2026-05-09","dateType":"Issued"}],"language":"en","types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.20099089","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Le processus d'inscription d'un bien sur la Liste du patrimoine mondial de l'UNESCO est fréquemment appréhendé comme une démarche essentiellement administrative et documentaire. Cette contribution soutient une thèse différente : l'inscription au patrimoine mondial est avant tout une démarche de gestion des risques. Démontrer la valeur universelle exceptionnelle d'un bien ne suffit pas ; encore faut-il établir que ce bien est protégé de manière efficace contre les menaces qui pèsent sur son intégrité et son authenticité. Cette articulation entre processus d'inscription et gestion des risques est particulièrement évidente dans le cas des sites archéologiques sous-marins, dont la vulnérabilité spécifique aux risques climatiques, tectoniques et anthropiques constitue un défi méthodologique majeur pour les équipes chargées de préparer les dossiers de candidature. S'appuyant sur une participation directe au montage du dossier d'inscription du site du Phare d'Alexandrie sur la Liste indicative de l'UNESCO, cet article examine les conditions méthodologiques d'une inscription crédible, analyse la place de la gestion des risques dans le processus UNESCO et formule des réflexions applicables à d'autres contextes de sites patrimoniaux vulnérables, notamment en Afrique centrale.\n\nMots-clés : inscription UNESCO, patrimoine archéologique sous-marin, gestion des risques, valeur universelle exceptionnelle, Liste indicative, Phare d'Alexandrie, conservation préventive, Méditerranée.","descriptionType":"Abstract"},{"lang":"eng","description":"The process of inscribing a property on UNESCO's World Heritage List is frequently perceived as an essentially administrative and documentary exercise. This contribution argues differently: World Heritage inscription is first and foremost a risk management process. Demonstrating outstanding universal value is not enough; it must also be established that the property is effectively protected against threats to its integrity and authenticity. This articulation between inscription and risk management is particularly evident in the case of underwater archaeological sites, whose specific vulnerability to climatic, tectonic and anthropogenic risks constitutes a major methodological challenge for teams responsible for preparing nomination files. Drawing on direct participation in the preparation of the nomination file for the Lighthouse of Alexandria site on UNESCO's Tentative List, this article examines the methodological conditions for a credible inscription, analyses the place of risk management in the UNESCO process and formulates reflections applicable to other contexts of vulnerable heritage sites, particularly in Central Africa.\n\nKeywords: UNESCO inscription, underwater archaeological heritage, risk management, outstanding universal value, Tentative List, Lighthouse of Alexandria, preventive conservation, Mediterranean.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.20099090","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-05-09T17:18:38Z","registered":"2026-05-09T17:18:38Z","published":null,"updated":"2026-05-09T17:18:38Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.20099089","type":"dois","attributes":{"doi":"10.5281/zenodo.20099089","identifiers":[],"creators":[{"name":"DOTCHE, Ameyo Mireille","nameType":"Personal","givenName":"Ameyo Mireille","familyName":"DOTCHE","nameIdentifiers":[],"affiliation":[]}],"titles":[{"title":"Inscrire un site archéologique sous-marin sur la Liste du patrimoine mondial de l'UNESCO : enjeux méthodologiques, conditions de protection et intégration de la gestion des risques. Réflexions à partir du cas du Phare d'Alexandrie"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Risk Management","subjectScheme":"MeSH"},{"subject":"Underwater archaeology","subjectScheme":"EuroSciVoc"},{"subject":"UNESCO","subjectScheme":"MeSH"},{"subject":"lighthouse of alexandria"},{"subject":"tentative list"},{"subject":"universal value"},{"subject":"conservation"}],"contributors":[],"dates":[{"date":"2026-05-09","dateType":"Issued"}],"language":"en","types":{"ris":"GEN","bibtex":"misc","citeproc":"article","schemaOrg":"CreativeWork","resourceType":"","resourceTypeGeneral":"Preprint"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.20099089","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Le processus d'inscription d'un bien sur la Liste du patrimoine mondial de l'UNESCO est fréquemment appréhendé comme une démarche essentiellement administrative et documentaire. Cette contribution soutient une thèse différente : l'inscription au patrimoine mondial est avant tout une démarche de gestion des risques. Démontrer la valeur universelle exceptionnelle d'un bien ne suffit pas ; encore faut-il établir que ce bien est protégé de manière efficace contre les menaces qui pèsent sur son intégrité et son authenticité. Cette articulation entre processus d'inscription et gestion des risques est particulièrement évidente dans le cas des sites archéologiques sous-marins, dont la vulnérabilité spécifique aux risques climatiques, tectoniques et anthropiques constitue un défi méthodologique majeur pour les équipes chargées de préparer les dossiers de candidature. S'appuyant sur une participation directe au montage du dossier d'inscription du site du Phare d'Alexandrie sur la Liste indicative de l'UNESCO, cet article examine les conditions méthodologiques d'une inscription crédible, analyse la place de la gestion des risques dans le processus UNESCO et formule des réflexions applicables à d'autres contextes de sites patrimoniaux vulnérables, notamment en Afrique centrale.\n\nMots-clés : inscription UNESCO, patrimoine archéologique sous-marin, gestion des risques, valeur universelle exceptionnelle, Liste indicative, Phare d'Alexandrie, conservation préventive, Méditerranée.","descriptionType":"Abstract"},{"lang":"eng","description":"The process of inscribing a property on UNESCO's World Heritage List is frequently perceived as an essentially administrative and documentary exercise. This contribution argues differently: World Heritage inscription is first and foremost a risk management process. Demonstrating outstanding universal value is not enough; it must also be established that the property is effectively protected against threats to its integrity and authenticity. This articulation between inscription and risk management is particularly evident in the case of underwater archaeological sites, whose specific vulnerability to climatic, tectonic and anthropogenic risks constitutes a major methodological challenge for teams responsible for preparing nomination files. Drawing on direct participation in the preparation of the nomination file for the Lighthouse of Alexandria site on UNESCO's Tentative List, this article examines the methodological conditions for a credible inscription, analyses the place of risk management in the UNESCO process and formulates reflections applicable to other contexts of vulnerable heritage sites, particularly in Central Africa.\n\nKeywords: UNESCO inscription, underwater archaeological heritage, risk management, outstanding universal value, Tentative List, Lighthouse of Alexandria, preventive conservation, Mediterranean.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.20099089","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-05-09T17:18:38Z","registered":"2026-05-09T17:18:38Z","published":null,"updated":"2026-05-09T17:18:38Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}}],"meta":{"total":105866,"totalPages":400,"page":1},"links":{"self":"https://api.datacite.org/dois?query=subjects.subjectScheme%3AEuroSciVoc","next":"https://api.datacite.org/dois?page%5Bnumber%5D=2\u0026page%5Bsize%5D=25\u0026query=subjects.subjectScheme%3AEuroSciVoc"}}