{"data":[{"id":"10.24357/igjr.12.1.25905","type":"dois","attributes":{"doi":"10.24357/igjr.12.1.25905","identifiers":[{"identifier":"17-2028-25905","identifierType":"publisherId"}],"creators":[{"name":"Aneni, Thomas","affiliation":[],"nameIdentifiers":[]},{"name":"Amabogha, Adumaro","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Beyond state policy: the personal carbon footprint, climate equity, and the bottom-up imperative in the African region and Nigeria"}],"publisher":"Intergenerational Justice Review","container":{"firstPage":"Vol. 12 No. 1 (2026): Personal Carbon Footprint (Part I)","type":"Series","title":"Intergenerational Justice Review"},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-07-20","dateType":"Submitted"},{"date":"2026-07-20","dateType":"Updated"},{"date":"2026-07-20","dateType":"Issued"}],"language":"en","types":{"schemaOrg":"ScholarlyArticle","resourceTypeGeneral":"Text","citeproc":"article-journal","bibtex":"article","ris":"RPRT","resourceType":"Article"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"State-centric, production-based climate governance has failed to deliver intergenerational justice, particularly for Africa. This article reframes the debate around the personal carbon footprint (PerCF), arguing that consumption-based accounting more accurately locates moral responsibility and that the subsistence-luxury distinction is essential for equitable policy in developing economies. A Nigerian case study illustrates both the structural constraints inflating involuntary emissions and the rising luxury footprint of an urban elite. Three reforms are proposed: a progressive bifurcated carbon tax, a national clean energy access fund, and a digital PerCF tracker."},{"descriptionType":"SeriesInformation","description":"Intergenerational Justice Review, Vol. 12 No. 1 (2026): Personal Carbon Footprint (Part I)"}],"geoLocations":[],"fundingReferences":[],"url":"https://igjr.org/ojs/index.php/igjr/article/view/25905","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-21T04:00:14Z","registered":"2026-07-21T04:00:15Z","published":null,"updated":"2026-07-21T04:00:15Z"},"relationships":{"client":{"data":{"id":"tib.ubtdois","type":"clients"}}}},{"id":"10.24357/igjr.12.1.25906","type":"dois","attributes":{"doi":"10.24357/igjr.12.1.25906","identifiers":[{"identifier":"17-2028-25906","identifierType":"publisherId"}],"creators":[{"name":"Ichihara, Masako","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Exploring the legal obligation to reduce personal greenhouse gas emissions through climate litigation rulings"}],"publisher":"Intergenerational Justice Review","container":{"firstPage":"Vol. 12 No. 1 (2026): Personal Carbon Footprint (Part I)","type":"Series","title":"Intergenerational Justice Review"},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-07-20","dateType":"Submitted"},{"date":"2026-07-20","dateType":"Updated"},{"date":"2026-07-20","dateType":"Issued"}],"language":"en","types":{"schemaOrg":"ScholarlyArticle","resourceTypeGeneral":"Text","citeproc":"article-journal","bibtex":"article","ris":"RPRT","resourceType":"Article"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Against the backdrop of an accelerating climate crisis, legal frameworks are maturing globally. Due to litigation, they already establish legal obligations for states and private corporations to implement appropriate reduction regulations and measures for their greenhouse gas emissions. In contrast, no globally agreed framework legally obligating individuals to reduce emissions has yet emerged. This article attempts to establish a legal basis for individual emission reduction obligations in light of this situation. Using international human rights conventions as a foundation, it argues that certain reduction obligations for individuals might exist, based on their respective circumstances. This conclusion is significant as a theoretical premise for incorporating individuals as key actors in future climate change countermeasures."},{"descriptionType":"SeriesInformation","description":"Intergenerational Justice Review, Vol. 12 No. 1 (2026): Personal Carbon Footprint (Part 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analysis"}],"publisher":"Redivis","container":{},"publicationYear":2022,"subjects":[{"subject":"climate"},{"subject":"global"}],"contributors":[],"dates":[{"date":"2022-11-09T17:27:38.452Z","dateType":"Created"},{"date":"2026-06-22T23:51:15.117Z","dateType":"Updated"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Workflow","citeproc":"article","bibtex":"misc","ris":"GEN"},"relatedIdentifiers":[{"relationType":"Cites","relatedIdentifier":"10.71778/V2DW-7A53","relatedIdentifierType":"DOI"},{"relationType":"References","resourceTypeGeneral":"Dataset","relatedIdentifier":"10.57761/h9ff-vy04","relatedIdentifierType":"DOI"},{"relationType":"References","resourceTypeGeneral":"Dataset","relatedIdentifier":"10.57761/c3tj-5646","relatedIdentifierType":"DOI"},{"relationType":"IsPartOf","resourceTypeGeneral":"Project","relatedIdentifier":"https://redivis.com/projects/3vkk-0nd7dkrjf","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":["2 data sources","2 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more](\u003chttps://docs.redivis.com/reference/workflows/notebooks\u003e)\n\n\n\n\n\n![map](\u003chttps://redivis.com/fileUploads/b9013038-23b7-40c9-b6bb-5092cbc795d3\u003e)\n\n"}],"geoLocations":[],"fundingReferences":[],"url":"https://redivis.com/workflows/x7kh-5pvd4mbf1","contentUrl":null,"metadataVersion":36,"schemaVersion":"http://datacite.org/schema/kernel-4.6","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":3,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2025-01-23T04:36:06Z","registered":"2025-02-01T00:00:01Z","published":null,"updated":"2026-07-21T04:00:04Z"},"relationships":{"client":{"data":{"id":"chvf.pbyfos","type":"clients"}}}},{"id":"10.48550/arxiv.2605.06964","type":"dois","attributes":{"doi":"10.48550/arxiv.2605.06964","identifiers":[{"identifier":"2605.06964","identifierType":"arXiv"}],"creators":[{"nameType":"Personal","givenName":"Jacob","familyName":"Haqq-Misra","name":"Haqq-Misra, Jacob","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Exploring TRAPPIST-1 Climate States with an Energy Balance Model"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"subject":"Earth and Planetary Astrophysics (astro-ph.EP)","lang":"en","subjectScheme":"arXiv"},{"subject":"Instrumentation and Methods for Astrophysics (astro-ph.IM)","lang":"en","subjectScheme":"arXiv"},{"subject":"FOS: Physical sciences","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-05-07T21:33:36Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-05-11T00:16:01Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-07-20T14:08:18Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2026-07-21T01:38:57Z","dateType":"Updated","dateInformation":"v2"},{"date":"2026-05","dateType":"Available","dateInformation":"v1"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":"Article"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"2","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This paper presents a version of the HEXTOR energy balance model that has been configured for the study of habitable terrestrial planets orbiting low-mass stars. The model is validated for rapidly-rotating Earth-like planets using latitudinal coordinates, which shows expected patterns of bistability. A tidally-locked coordinate transformation is then applied to the model, which is calibrated to match mean values of the minimum, average, and maximum surface temperatures from a general circulation model ensemble of TRAPPIST-1 e. This calibrated energy balance model is used to characterize the possible climate states of such a synchronously rotating planet across a parameter space of instellation and carbon dioxide partial pressure. These calculations suggest a state of partial ice cover for TRAPPIST-1 e and complete ice cover for TRAPPIST-1 f. TRAPPIST-1 e becomes fully ice-free only above ~0.4 bar CO$_2$, while TRAPPIST-1 f remains ice-covered unless CO$_2$ partial pressure approaches ~1.2 bar. This approach demonstrates the capability of a simplified one-dimensional model to study the climates of terrestrial planets in synchronous rotation, which can help guide more complex models and observations toward the most promising targets of interest."},{"descriptionType":"Other","description":"Published in the Open Journal of Astrophysics"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2605.06964","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-05-11T02:08:46Z","registered":"2026-05-11T02:08:46Z","published":null,"updated":"2026-07-21T03:50:36Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2604.26884","type":"dois","attributes":{"doi":"10.48550/arxiv.2604.26884","identifiers":[{"identifier":"2604.26884","identifierType":"arXiv"}],"creators":[{"nameType":"Personal","givenName":"Danny","familyName":"Parsons","name":"Parsons, Danny","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"David","familyName":"Stern","name":"Stern, David","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Mouhamadou Bamba","familyName":"Sylla","name":"Sylla, Mouhamadou Bamba","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"James","familyName":"Musyoka","name":"Musyoka, James","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"John","familyName":"Bagiliko","name":"Bagiliko, John","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Lily","familyName":"Clements","name":"Clements, Lily","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"John","familyName":"Mupuro","name":"Mupuro, John","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Denis","familyName":"Ndanguza","name":"Ndanguza, Denis","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Improving Bias Correction Methods for Daily Rainfall Using a Markov Chain Approach"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"subject":"Applications (stat.AP)","lang":"en","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-04-29T16:53:05Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-04-30T01:00:48Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-07-20T11:27:08Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2026-07-21T01:30:55Z","dateType":"Updated","dateInformation":"v2"},{"date":"2026-04","dateType":"Available","dateInformation":"v1"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":"Article"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"2","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution Non Commercial No Derivatives 4.0 International","rightsIdentifier":"cc-by-nc-nd-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Accurate, localised rainfall information is essential for agricultural planning, climate risk assessment, and water resources management. Gridded climate products provide rainfall information over large areas but can lack the accuracy needed at local scales, often requiring bias correction before use in local impact studies. Local intensity scaling (LOCI) and quantile mapping (QM) are two widely used bias correction methods which adjust both rainfall frequency and intensity, but do not account for the temporal structure of daily rainfall. This can lead to biases in the representation of wet and dry spells. This study proposes integrating a two-state first-order Markov chain into existing bias correction methods through state-dependent rain day thresholds and rainfall adjustments, aimed at improving temporal structure. Two implementations of this framework are presented: Markov chain local intensity scaling (MC LOCI) and Markov chain quantile mapping (MC QM). The proposed methods were applied to AgERA5 reanalysis data with rainfall data from five stations in Zimbabwe. Results showed that the Markov chain methods improved the representation of rainfall persistence, onset, and wet and dry spell characteristics compared to LOCI and QM, while maintaining improvements in rain day frequency, mean and total rainfall. Improvements in event timing and daily rainfall amounts were limited. Results from five locations in Zimbabwe demonstrate that the proposed methods could be beneficial for crop simulation, hydrological modelling and other applications requiring accurate rainfall sequencing. Evaluation across additional regions and gridded products would establish the broader applicability of the proposed methods under a range of conditions."},{"descriptionType":"Other","description":"44 pages, 19 figures"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2604.26884","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-30T02:09:36Z","registered":"2026-04-30T02:09:37Z","published":null,"updated":"2026-07-21T03:50:13Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2604.22328","type":"dois","attributes":{"doi":"10.48550/arxiv.2604.22328","identifiers":[{"identifier":"2604.22328","identifierType":"arXiv"}],"creators":[{"nameType":"Personal","givenName":"Marco","familyName":"Obermeier","name":"Obermeier, Marco","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Marco","familyName":"Pruckner","name":"Pruckner, Marco","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Florian","familyName":"Haselbeck","name":"Haselbeck, Florian","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Andreas","familyName":"Zeiselmair","name":"Zeiselmair, Andreas","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"subject":"Machine Learning (cs.LG)","lang":"en","subjectScheme":"arXiv"},{"subject":"Artificial Intelligence (cs.AI)","lang":"en","subjectScheme":"arXiv"},{"subject":"Computational Engineering, Finance, and Science (cs.CE)","lang":"en","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-04-24T08:00:50Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-04-27T00:27:11Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-07-17T22:36:22Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2026-07-21T00:13:25Z","dateType":"Updated","dateInformation":"v2"},{"date":"2026-04","dateType":"Available","dateInformation":"v1"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":"Article"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"2","rightsList":[{"rightsUri":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","rights":"arXiv.org perpetual, non-exclusive license"}],"descriptions":[{"descriptionType":"Abstract","description":"Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operations. Yet, it remains a dataset-specific task, requiring comprehensive training data, limiting scalability, and resulting in high model development and maintenance effort. Recently, foundation models aiming to learn generalizable patterns via extensive pretraining have shown strong performance in multiple prediction tasks. Despite their success and strong potential in energy forecasting, a systematic, use-case-differentiated evaluation is still missing. We address this gap by presenting the Foundation Models in Energy Time Series Forecasting (FETS) benchmark. We (1) provide a structured overview of energy forecasting use cases along three main dimensions, i.e., stakeholders, attributes, and data categories, (2) curate 54 datasets across 9 data categories, guided by typical stakeholder interests, and (3) benchmark foundation models against task-specific machine learning across different forecasting settings. In our benchmark study, covariate-informed zero-shot foundation models perform best in aggregate, with Chronos-2 attaining the lowest overall median NRMSE (0.472), closely followed by TiRex-2 (0.474). Both perform better than XGBoost (0.611) and random forest (0.696), although they were trained task-specifically on the full historic target data. Further analysis reveals a strong correlation between predictive performance and spectral entropy. Performance saturates beyond a certain context length and improves with aggregation level, e.g., for national load, district heating, and power grid data. Overall, with the lowest median error, limited data requirements, and low inference and hardware demands, foundation models reduce development and maintenance effort, emerging as scalable and generalizable energy forecasting solutions."}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2604.22328","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-27T01:34:43Z","registered":"2026-04-27T01:34:44Z","published":null,"updated":"2026-07-21T03:50:02Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2511.17663","type":"dois","attributes":{"doi":"10.48550/arxiv.2511.17663","identifiers":[{"identifier":"2511.17663","identifierType":"arXiv"}],"creators":[{"nameType":"Personal","givenName":"Alex S. C.","familyName":"Maia","name":"Maia, Alex S. C.","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"John B.","familyName":"Hall","name":"Hall, John B.","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Hugo F. M.","familyName":"Milan","name":"Milan, Hugo F. M.","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Izabelle A. M. A.","familyName":"Teixeira","name":"Teixeira, Izabelle A. M. A.","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"AI-based framework to predict animal and pen feed intake in feedlot beef cattle"}],"publisher":"arXiv","container":{},"publicationYear":2025,"subjects":[{"subject":"Machine Learning (cs.LG)","lang":"en","subjectScheme":"arXiv"},{"subject":"Artificial Intelligence (cs.AI)","lang":"en","subjectScheme":"arXiv"},{"subject":"Systems and Control (eess.SY)","lang":"en","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Electrical engineering, electronic engineering, information engineering","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2025-11-20T22:43:53Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-07-21T01:01:29Z","dateType":"Updated","dateInformation":"v1"},{"date":"2025-11","dateType":"Available","dateInformation":"v1"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":"Article"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.1016/j.atech.2026.102090","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution Share Alike 4.0 International","rightsIdentifier":"cc-by-sa-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Advances in technology are transforming sustainable cattle farming practices, with electronic feeding systems generating big longitudinal datasets on individual animal feed intake, offering the possibility for autonomous precision livestock systems. However, the literature still lacks a methodology that fully leverages these longitudinal big data to accurately predict feed intake accounting for environmental conditions. To fill this gap, we developed an AI-based framework to accurately predict feed intake of individual animals and pen-level aggregation. Data from 19 experiments (\u0026gt;16.5M samples; 2013-2024) conducted at Nancy M. Cummings Research Extension \u0026amp; Education Center (Carmen, ID) feedlot facility and environmental data from AgriMet Network weather stations were used to develop two novel environmental indices: InComfort-Index, based solely on meteorological variables, showed good predictive capability for thermal comfort but had limited ability to predict feed intake; EASI-Index, a hybrid index integrating environmental variables with feed intake behavior, performed well in predicting feed intake but was less effective for thermal comfort. Together with the environmental indices, machine learning models were trained and the best-performing machine learning model (XGBoost) accuracy was RMSE of 1.38 kg/day for animal-level and only 0.14 kg/(day-animal) at pen-level. This approach provides a robust AI-based framework for predicting feed intake in individual animals and pens, with potential applications in precision management of feedlot cattle, through feed waste reduction, resource optimization, and climate-adaptive livestock management."}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2511.17663","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2025-11-25T03:53:56Z","registered":"2025-11-25T03:53:57Z","published":null,"updated":"2026-07-21T03:44:40Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2511.17134","type":"dois","attributes":{"doi":"10.48550/arxiv.2511.17134","identifiers":[{"identifier":"2511.17134","identifierType":"arXiv"}],"creators":[{"nameType":"Personal","givenName":"Sonia","familyName":"Dupuis","name":"Dupuis, Sonia","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Nando","familyName":"Metzger","name":"Metzger, Nando","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Konrad","familyName":"Schindler","name":"Schindler, Konrad","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Frank","familyName":"Göttsche","name":"Göttsche, Frank","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Stefan","familyName":"Wunderle","name":"Wunderle, Stefan","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Four decades of circumpolar super-resolved satellite land surface temperature data"}],"publisher":"arXiv","container":{},"publicationYear":2025,"subjects":[{"subject":"Machine Learning (cs.LG)","lang":"en","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2025-11-21T10:53:19Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-07-21T01:28:15Z","dateType":"Updated","dateInformation":"v1"},{"date":"2025-11","dateType":"Available","dateInformation":"v1"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":"Article"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.1038/s41597-026-07399-6","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Land surface temperature (LST) is an essential climate variable (ECV) crucial for understanding land-atmosphere energy exchange and monitoring climate change, especially in the rapidly warming Arctic. Long-term satellite-based LST records, such as those derived from the Advanced Very High Resolution Radiometer (AVHRR), are essential for detecting climate trends. However, the coarse spatial resolution of AVHRR's global area coverage (GAC) data limit their utility for analyzing fine-scale permafrost dynamics and other surface processes in the Arctic. This paper presents a new 42 years pan-Arctic LST dataset, downscaled from AVHRR GAC to 1 km with a super-resolution algorithm based on a deep anisotropic diffusion model. The model is trained on MODIS LST data, using coarsened inputs and native-resolution outputs, guided by high-resolution land cover, digital elevation, and vegetation height maps. The resulting dataset provides twice-daily, 1 km LST observations for the entire pan-Arctic region over four decades. This enhanced dataset enables improved modelling of permafrost, reconstruction of near-surface air temperature, and assessment of surface mass balance of the Greenland Ice Sheet. Additionally, it supports climate monitoring efforts in the pre-MODIS era and offers a framework adaptable to future satellite missions for thermal infrared observation and climate data record continuity."}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2511.17134","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2025-11-24T02:33:28Z","registered":"2025-11-24T02:33:29Z","published":null,"updated":"2026-07-21T03:44:39Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2511.15689","type":"dois","attributes":{"doi":"10.48550/arxiv.2511.15689","identifiers":[{"identifier":"2511.15689","identifierType":"arXiv"}],"creators":[{"nameType":"Personal","givenName":"Jason R.","familyName":"Blevins","name":"Blevins, Jason R.","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Semiparametric Estimation of Fractional Integration: An Evaluation of Local Whittle Methods"}],"publisher":"arXiv","container":{},"publicationYear":2025,"subjects":[{"subject":"Econometrics (econ.EM)","lang":"en","subjectScheme":"arXiv"},{"subject":"FOS: Economics and business","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2025-11-19T18:45:21Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2025-11-20T02:04:27Z","dateType":"Updated","dateInformation":"v1"},{"date":"2025-12-15T22:26:22Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2025-12-17T01:09:52Z","dateType":"Updated","dateInformation":"v2"},{"date":"2026-07-03T21:11:31Z","dateType":"Submitted","dateInformation":"v3"},{"date":"2026-07-07T01:00:51Z","dateType":"Updated","dateInformation":"v3"},{"date":"2026-07-20T02:00:36Z","dateType":"Submitted","dateInformation":"v4"},{"date":"2026-07-21T01:10:52Z","dateType":"Updated","dateInformation":"v4"},{"date":"2025-11","dateType":"Available","dateInformation":"v1"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":"Article"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"4","rightsList":[{"rightsUri":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","rights":"arXiv.org perpetual, non-exclusive license"}],"descriptions":[{"descriptionType":"Abstract","description":"Fractionally integrated time series, exhibiting long memory with slowly decaying autocorrelations, are frequently encountered in economics, finance, and related fields. Since the seminal work of Robinson (1995), a variety of semiparametric local Whittle estimators have been proposed for estimating the memory parameter $d$, each with a distinct range of validity and different robustness properties, leaving applied researchers to decide which to use and under what conditions. This paper offers a practitioner's guide to six such estimators. Using a common Monte Carlo design, we map how each estimator behaves under short-run dynamics, unknown means, and time trends -- the conditions under which each remains reliable and the characteristic way each breaks down. This reveals a tension between efficiency and robustness: the exact local Whittle estimator uniquely pairs the lowest asymptotic variance with an unrestricted parameter range, but requires the mean and trend to be handled with care. We then illustrate these failure modes, along with the difficulties introduced by structural breaks, on several macroeconomic, financial, and climate time series, where a naïvely applied estimator can report near-stationarity for a series that better-matched methods identify as strongly nonstationary. The resulting guidance on estimator choice and bandwidth selection is anchored by exact reproductions of published results from this literature, along with open source replication code and datasets."}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2511.15689","contentUrl":null,"metadataVersion":3,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2025-11-20T03:02:45Z","registered":"2025-11-20T03:02:46Z","published":null,"updated":"2026-07-21T03:44:34Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2510.04748","type":"dois","attributes":{"doi":"10.48550/arxiv.2510.04748","identifiers":[{"identifier":"2510.04748","identifierType":"arXiv"}],"creators":[{"nameType":"Personal","givenName":"Florence E.","familyName":"Enock","name":"Enock, Florence E.","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Helen Z.","familyName":"Margetts","name":"Margetts, Helen Z.","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Jonathan","familyName":"Bright","name":"Bright, Jonathan","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Ingroup bias is prevalent in user reports of hate and abuse online"}],"publisher":"arXiv","container":{},"publicationYear":2025,"subjects":[{"subject":"Computers and Society (cs.CY)","lang":"en","subjectScheme":"arXiv"},{"subject":"Human-Computer Interaction (cs.HC)","lang":"en","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2025-10-06T12:27:19Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2025-10-07T01:30:37Z","dateType":"Updated","dateInformation":"v1"},{"date":"2025-10-07T15:07:13Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2025-10-08T00:53:49Z","dateType":"Updated","dateInformation":"v2"},{"date":"2026-07-20T11:33:43Z","dateType":"Submitted","dateInformation":"v3"},{"date":"2026-07-21T01:31:14Z","dateType":"Updated","dateInformation":"v3"},{"date":"2025-10","dateType":"Available","dateInformation":"v1"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":"Article"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.1098/rsos.251490","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"3","rightsList":[{"rightsUri":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","rights":"arXiv.org perpetual, non-exclusive license"}],"descriptions":[{"descriptionType":"Abstract","description":"The prevalence of online hate and abuse is a pressing global problem. While tackling such societal harms is a priority for research across the social sciences, it is a difficult task, in part because of the magnitude of the problem. People's engagement with reporting mechanisms ('flagging') online is an increasingly important part of monitoring and addressing harmful content at scale. However, users may not flag content routinely enough, and when users do engage, they may be biased by group identity and political beliefs. Across five well-powered and pre-registered online experiments, we examine the extent of ingroup bias in people's flagging of hate and abuse in four different intergroup contexts: political affiliation, vaccination opinions, beliefs about climate change, and stance on abortion rights. Overall, participants reported abuse reliably, with approximately half of the abusive comments in each study reported. However, a pervasive ingroup bias was present whereby across studies, participants were between 17% and 63% more likely to flag abuse directed at the ingroup than at the outgroup. Our findings offer new insights into the nature of user flagging online, an understanding of which is crucial for enhancing user intervention against online hate and thus ensuring a safer online environment."},{"descriptionType":"Other","description":"Accepted for publication in Royal Society Open Science (June 2026). This is the accepted manuscript prior to copyediting and typesetting"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2510.04748","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2025-10-07T03:22:06Z","registered":"2025-10-07T03:22:06Z","published":null,"updated":"2026-07-21T03:43:17Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2403.06025","type":"dois","attributes":{"doi":"10.48550/arxiv.2403.06025","identifiers":[{"identifier":"2403.06025","identifierType":"arXiv"}],"creators":[{"nameType":"Personal","givenName":"Wei","familyName":"Chen","name":"Chen, Wei","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Yunan","familyName":"Li","name":"Li, Yunan","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Yuan","familyName":"Tian","name":"Tian, Yuan","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"CarbonNet: How Computer Vision Plays a Role in Climate Change? Application: Learning Geomechanics from Subsurface Geometry of CCS to Mitigate Global Warming"}],"publisher":"arXiv","container":{},"publicationYear":2024,"subjects":[{"subject":"Computer Vision and Pattern Recognition (cs.CV)","lang":"en","subjectScheme":"arXiv"},{"subject":"Artificial Intelligence (cs.AI)","lang":"en","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2024-03-09T22:25:14Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2024-03-12T00:39:15Z","dateType":"Updated","dateInformation":"v1"},{"date":"2024-03-12T17:35:29Z","dateType":"Submitted","dateInformation":"v2"},{"date":"2024-03-13T01:03:32Z","dateType":"Updated","dateInformation":"v2"},{"date":"2024-03-19T05:58:51Z","dateType":"Submitted","dateInformation":"v3"},{"date":"2024-03-20T00:36:30Z","dateType":"Updated","dateInformation":"v3"},{"date":"2026-07-20T02:28:29Z","dateType":"Submitted","dateInformation":"v4"},{"date":"2026-07-21T01:12:08Z","dateType":"Updated","dateInformation":"v4"},{"date":"2024-03","dateType":"Available","dateInformation":"v1"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":"Article"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"4","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"We introduce a new approach using computer vision to predict the land surface displacement from subsurface geometry images for Carbon Capture and Sequestration (CCS). CCS has been proved to be a key component for a carbon neutral society. However, scientists see there are challenges along the way including the high computational cost due to the large model scale and limitations to generalize a pre-trained model with complex physics. We tackle those challenges by training models directly from the subsurface geometry images. The goal is to understand the respons of land surface displacement due to carbon injection and utilize our trained models to inform decision making in CCS projects.\n We implement multiple models (CNN, ResNet, and ResNetUNet) for static mechanics problem, which is a image prediction problem. Next, we use the LSTM and transformer for transient mechanics scenario, which is a video prediction problem. It shows ResNetUNet outperforms the others thanks to its architecture in static mechanics problem, and LSTM shows comparable performance to transformer in transient problem. This report proceeds by outlining our dataset in detail followed by model descriptions in method section. Result and discussion state the key learning, observations, and conclusion with future work rounds out the paper."}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2403.06025","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2024-03-12T02:49:35Z","registered":"2024-03-12T02:49:35Z","published":null,"updated":"2026-07-21T03:38:06Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2607.18170","type":"dois","attributes":{"doi":"10.48550/arxiv.2607.18170","identifiers":[{"identifier":"2607.18170","identifierType":"arXiv"}],"creators":[{"nameType":"Personal","givenName":"Carsten","familyName":"Orwat","name":"Orwat, Carsten","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Lucas","familyName":"Staab","name":"Staab, Lucas","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Alexandros","familyName":"Gazos","name":"Gazos, Alexandros","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"An approach to systemic risks of AI through the lens of emergence, collective action problems, and externalities"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"subject":"Computers and Society (cs.CY)","lang":"en","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-07-20T17:12:04Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-07-21T01:49:53Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-07","dateType":"Available","dateInformation":"v1"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":"Article"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"1","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution Non Commercial No Derivatives 4.0 International","rightsIdentifier":"cc-by-nc-nd-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"The integration of general-purpose artificial intelligence models into downstream AI systems, among other developments, has given rise to new forms of risk that are more systemic in nature than conventional AI risks. However, there is no generally accepted definition of systemic risks in general and for AI in particular. Conceptualisations of these risks vary across research and regulation. Especially the application of the systemic risk approach to human rights or fundamental rights, like in the EU AI Act, is relatively new, just as the research on the contribution of AI to systemic forms of discrimination, privacy violations, erosions of democracy, or climate and environmental degradation. We argue that some concepts so far have not sufficiently take complexity and emergence into account. Furthermore, this variety of concepts might hinder responsible actors to adequately assess the systemic risks of AI, leading to inadequate prevention and mitigation measures and ineffective governance. To contribute to the understanding of systemic risks of AI, we propose a conceptualisation of systemic risks of AI that considers complex phenomena that lead to the emergence of harms at the societal or global level. We outline systemic risks mainly as complex externalities and collective action problems. Of particular interest are feedback dynamics, processes that lead to market concentration like network effects, algorithmic monocultures, and integration processes of AI supply chains or 'AI ecosystems' and across societal sectors, which can result in structural dominance, (inter-) dependencies, and cascading risks. Further phenomena contributing to systemic risks are information asymmetries, informational emergence, and deficits of the governance and institutional framework."},{"descriptionType":"Other","description":"42 pages"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2607.18170","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-21T03:34:59Z","registered":"2026-07-21T03:35:00Z","published":null,"updated":"2026-07-21T03:35:00Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.5281/zenodo.21466191","type":"dois","attributes":{"doi":"10.5281/zenodo.21466191","identifiers":[{"identifier":"oai:zenodo.org:21466191","identifierType":"oai"}],"creators":[{"nameType":"Personal","givenName":"Gu","familyName":"hongshuang","name":"hongshuang, Gu","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0003-0609-7862"}],"affiliation":[]}],"titles":[{"title":"Code for paper \"Reversing effects of seasonal warming on autumn phenology across the Northern Hemisphere\""}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"phenology"}],"contributors":[],"dates":[{"date":"2026-07-21","dateType":"Issued"}],"language":null,"types":{"schemaOrg":"SoftwareSourceCode","resourceTypeGeneral":"Software","citeproc":"article","bibtex":"misc","ris":"COMP","resourceType":""},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.16925154","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This dataset accompanies the manuscript“Reversing effects of seasonal warming on autumn phenology across the Northern Hemisphere.” It provides the core R scripts required to reproduce the main analyses presented in the manuscript, including data curation and extraction, temperature sensitivity analysis, mixed-effects modeling, random forest analysis, phenology curve fitting for the warming control experiment, and the corresponding plotting scripts, together with the public demo datasets and the warming experiment data used in the analyses.\n\nScript Description\n\n1.         Autumn phenology extraction for Experiment.r: Extracts autumn phenology dates for the warming control experiment by fitting chlorophyll content curves.\n\n2.         Ground demo.r, GOSIF demo.r, MODIS demo.r, and NorESM2-MM demo.r: Core workflows for temperature sensitivity calculation, identification of warming sites/pixels, and definition of cold/warm regions and periods, based on ground-based, remote-sensing-based, and future-projection phenology data (illustrated using NorESM2-MM demo data).\n\n3.         Mixed linear models for PEP725.r: Core workflow for mixed-effects model analysis based on PEP725 data.\n\n4.         Random Forest \u0026 SHAP demo.r: Core workflow for random forest and SHAP analysis (illustrated using Ground demo data).\n\n5.         Fig for Experiment.r: Computation and plotting script for the warming control experiment results.\n\n6.         Fig for ground-based datasets.r: Plotting script for ground-based phenology data.\n\n7.         Fig for GOSIF and MODIS.r: Plotting script for remote-sensing-based phenology data.\n\n8.         Fig for FutureSSP.r: Plotting script for future phenology projections (illustrated using NorESM2-MM data).\n\n9.         Fig for RF-SHAP.r: Plotting script for random forest analysis results.\n\nData Description\n\n1.        Ground_demo.csv, gosif_demo.csv, modis_demo.csv, and noresm_demo.csv contain phenology and spring/autumn climate variables data for ground-based observations, remote sensing (GOSIF, MODIS), and future Earth System Model projections (illustrated using NorESM2-MM), respectively.\n\n2.        Ground_tmean_demo.csv, gosif_tmean_demo.csv, modis_tmean_demo.csv, and noresm_tmean_demo.csv contain the corresponding monthly mean temperature data for ground-based, remote-sensing-based, and future ESM datasets, used primarily to define cold/warm regions and periods.\n\n3.        Experiment_statistic.xlsx contains the temperature records, autumn phenology, treatment-control temperature differences, NSC concentrations, and CCI and leaf-retention data from the warming control experiment."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21466191","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":"2026-07-21T03:34:09Z","registered":"2026-07-21T03:34:10Z","published":null,"updated":"2026-07-21T03:34:10Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.16925154","type":"dois","attributes":{"doi":"10.5281/zenodo.16925154","identifiers":[],"creators":[{"nameType":"Personal","givenName":"Gu","familyName":"hongshuang","name":"hongshuang, Gu","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0000-0003-0609-7862"}],"affiliation":[]}],"titles":[{"title":"Code for paper \"Reversing effects of seasonal warming on autumn phenology across the Northern Hemisphere\""}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"phenology"}],"contributors":[],"dates":[{"date":"2026-07-21","dateType":"Issued"}],"language":"en","types":{"schemaOrg":"SoftwareSourceCode","resourceTypeGeneral":"Software","citeproc":"article","bibtex":"misc","ris":"COMP","resourceType":""},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.16925154","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This dataset accompanies the manuscript“Reversing effects of seasonal warming on autumn phenology across the Northern Hemisphere.” It provides the core R scripts required to reproduce the main analyses presented in the manuscript, including data curation and extraction, temperature sensitivity analysis, mixed-effects modeling, random forest analysis, phenology curve fitting for the warming control experiment, and the corresponding plotting scripts, together with the public demo datasets and the warming experiment data used in the analyses.\n\nScript Description\n\n1.         Autumn phenology extraction for Experiment.r: Extracts autumn phenology dates for the warming control experiment by fitting chlorophyll content curves.\n\n2.         Ground demo.r, GOSIF demo.r, MODIS demo.r, and NorESM2-MM demo.r: Core workflows for temperature sensitivity calculation, identification of warming sites/pixels, and definition of cold/warm regions and periods, based on ground-based, remote-sensing-based, and future-projection phenology data (illustrated using NorESM2-MM demo data).\n\n3.         Mixed linear models for PEP725.r: Core workflow for mixed-effects model analysis based on PEP725 data.\n\n4.         Random Forest \u0026 SHAP demo.r: Core workflow for random forest and SHAP analysis (illustrated using Ground demo data).\n\n5.         Fig for Experiment.r: Computation and plotting script for the warming control experiment results.\n\n6.         Fig for ground-based datasets.r: Plotting script for ground-based phenology data.\n\n7.         Fig for GOSIF and MODIS.r: Plotting script for remote-sensing-based phenology data.\n\n8.         Fig for FutureSSP.r: Plotting script for future phenology projections (illustrated using NorESM2-MM data).\n\n9.         Fig for RF-SHAP.r: Plotting script for random forest analysis results.\n\nData Description\n\n1.        Ground_demo.csv, gosif_demo.csv, modis_demo.csv, and noresm_demo.csv contain phenology and spring/autumn climate variables data for ground-based observations, remote sensing (GOSIF, MODIS), and future Earth System Model projections (illustrated using NorESM2-MM), respectively.\n\n2.        Ground_tmean_demo.csv, gosif_tmean_demo.csv, modis_tmean_demo.csv, and noresm_tmean_demo.csv contain the corresponding monthly mean temperature data for ground-based, remote-sensing-based, and future ESM datasets, used primarily to define cold/warm regions and periods.\n\n3.        Experiment_statistic.xlsx contains the temperature records, autumn phenology, treatment-control temperature differences, NSC concentrations, and CCI and leaf-retention data from the warming control experiment."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.16925154","contentUrl":null,"metadataVersion":5,"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":4,"versionOfCount":1,"created":"2025-08-22T08:12:56Z","registered":"2025-08-22T08:12:57Z","published":null,"updated":"2026-07-21T03:34:09Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.21466165","type":"dois","attributes":{"doi":"10.5281/zenodo.21466165","identifiers":[{"identifier":"oai:zenodo.org:21466165","identifierType":"oai"}],"creators":[{"nameType":"Personal","affiliation":["Lecturer, Department of English, Government College Women University, Faisalabad, Pakistan."],"familyName":"Aqdas Khanam","name":"Aqdas Khanam","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Lecturer in English, Department of English, University of Southern Punjab, Multan, Pakistan."],"familyName":"Saniya Fatima Gilani","name":"Saniya Fatima Gilani","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Associate Professor, Department of Arabic, Bahauddin Zakariya University, Multan, Pakistan."],"familyName":"Dr. Muhammad Sarwar","name":"Dr. Muhammad Sarwar","nameIdentifiers":[]}],"titles":[{"title":"ECOLOGICAL CRISIS AND HUMAN RESILIENCE IN CAMP ZERO: AN ECOCRITICAL STUDY"}],"publisher":"IPJLL","container":{},"publicationYear":2026,"subjects":[{"subject":"Anthropocene"},{"subject":"Camp Zero"},{"subject":"Climate Change"},{"subject":"Ecocriticism"},{"subject":"Ecological Crisis"},{"subject":"Environmental Degradation"},{"subject":"Human Resilience"}],"contributors":[],"dates":[{"date":"2026-03-31","dateType":"Issued"}],"language":"en","types":{"schemaOrg":"ScholarlyArticle","resourceTypeGeneral":"JournalArticle","citeproc":"article-journal","bibtex":"article","ris":"JOUR","resourceType":""},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.21466164","relatedIdentifierType":"DOI"},{"relationType":"IsPartOf","resourceTypeGeneral":"Collection","relatedIdentifier":"3007-2344","relatedIdentifierType":"ISSN"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This study examines Michelle Min Sterling's Camp Zero (2023) through the theoretical framework of ecocriticism, with particular emphasis on the representation of ecological crisis and human resilience. The study explores how the novel portrays the consequences of climate change, industrialization, environmental degradation, and anthropocentric exploitation of nature. Using qualitative textual analysis, the research analyzes the novel's language, imagery, metaphors, symbolism, and narrative descriptions to investigate the relationship between humans and the natural environment. The findings reveal that Camp Zero presents nature as a damaged yet significant force that reflects the long-term consequences of human negligence and unsustainable development. Images of polluted skies, contaminated water, melting ice, industrial expansion, and declining biodiversity highlight the severity of the ecological crisis and demonstrate the interconnectedness of environmental, social, and economic challenges. At the same time, the novel portrays human resilience through the characters' capacity to adapt, cooperate, and endure in the face of environmental uncertainty. Rather than depicting resilience as the complete triumph over ecological collapse, the narrative emphasizes adaptation, hope, and collective responsibility as essential for survival. The study concludes that Camp Zero functions as a powerful critique of environmental exploitation while advocating ecological consciousness and sustainable coexistence between humanity and the natural world. It contributes to contemporary ecocritical scholarship by offering a critical interpretation of a recent climate-fiction novel."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21466165","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":"2026-07-21T03:31:34Z","registered":"2026-07-21T03:31:34Z","published":null,"updated":"2026-07-21T03:31:34Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.21466164","type":"dois","attributes":{"doi":"10.5281/zenodo.21466164","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Lecturer, Department of English, Government College Women University, Faisalabad, Pakistan."],"familyName":"Aqdas Khanam","name":"Aqdas Khanam","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Lecturer in English, Department of English, University of Southern Punjab, Multan, Pakistan."],"familyName":"Saniya Fatima Gilani","name":"Saniya Fatima Gilani","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Associate Professor, Department of Arabic, Bahauddin Zakariya University, Multan, Pakistan."],"familyName":"Dr. Muhammad Sarwar","name":"Dr. Muhammad Sarwar","nameIdentifiers":[]}],"titles":[{"title":"ECOLOGICAL CRISIS AND HUMAN RESILIENCE IN CAMP ZERO: AN ECOCRITICAL STUDY"}],"publisher":"IPJLL","container":{},"publicationYear":2026,"subjects":[{"subject":"Anthropocene"},{"subject":"Camp Zero"},{"subject":"Climate Change"},{"subject":"Ecocriticism"},{"subject":"Ecological Crisis"},{"subject":"Environmental Degradation"},{"subject":"Human Resilience"}],"contributors":[],"dates":[{"date":"2026-03-31","dateType":"Issued"}],"language":"en","types":{"schemaOrg":"ScholarlyArticle","resourceTypeGeneral":"JournalArticle","citeproc":"article-journal","bibtex":"article","ris":"JOUR","resourceType":""},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.21466164","relatedIdentifierType":"DOI"},{"relationType":"IsPartOf","resourceTypeGeneral":"Collection","relatedIdentifier":"3007-2344","relatedIdentifierType":"ISSN"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This study examines Michelle Min Sterling's Camp Zero (2023) through the theoretical framework of ecocriticism, with particular emphasis on the representation of ecological crisis and human resilience. The study explores how the novel portrays the consequences of climate change, industrialization, environmental degradation, and anthropocentric exploitation of nature. Using qualitative textual analysis, the research analyzes the novel's language, imagery, metaphors, symbolism, and narrative descriptions to investigate the relationship between humans and the natural environment. The findings reveal that Camp Zero presents nature as a damaged yet significant force that reflects the long-term consequences of human negligence and unsustainable development. Images of polluted skies, contaminated water, melting ice, industrial expansion, and declining biodiversity highlight the severity of the ecological crisis and demonstrate the interconnectedness of environmental, social, and economic challenges. At the same time, the novel portrays human resilience through the characters' capacity to adapt, cooperate, and endure in the face of environmental uncertainty. Rather than depicting resilience as the complete triumph over ecological collapse, the narrative emphasizes adaptation, hope, and collective responsibility as essential for survival. The study concludes that Camp Zero functions as a powerful critique of environmental exploitation while advocating ecological consciousness and sustainable coexistence between humanity and the natural world. It contributes to contemporary ecocritical scholarship by offering a critical interpretation of a recent climate-fiction novel."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21466164","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":"2026-07-21T03:31:34Z","registered":"2026-07-21T03:31:34Z","published":null,"updated":"2026-07-21T03:31:34Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.71826/fc59022c","type":"dois","attributes":{"doi":"10.71826/fc59022c","identifiers":[],"creators":[{"nameType":"Personal","name":"Veena Sahajwalla","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org/","nameIdentifier":"https://orcid.org/0000-0001-9528-9967"}],"affiliation":[]},{"nameType":"Personal","name":"Anirban Ghose","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org/","nameIdentifier":"https://orcid.org/0009-0006-1511-9129"}],"affiliation":[]},{"nameType":"Personal","name":"Lucas Yinting Way","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org/","nameIdentifier":"https://orcid.org/0009-0009-3538-6541"}],"affiliation":[]},{"nameType":"Personal","name":"William Abbey","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org/","nameIdentifier":"https://orcid.org/0009-0008-3612-153X"}],"affiliation":[]}],"titles":[{"lang":"eng","title":"NESP SCaW Project IP2.02.02 - Finding fit-for-purpose technological recycling solutions for regional and remote communities across Australia., 2022-2026 (UNSW)"}],"publisher":"Department of Climate Change, Energy, the Environment and Water","container":{},"publicationYear":2026,"subjects":[],"contributors":[{"nameType":"Organizational","name":"Australian Research Data Commons","nameIdentifiers":[{"nameIdentifierScheme":"ROR","schemeUri":"https://ror.org/","nameIdentifier":"https://ror.org/038sjwq14"}],"contributorType":"RegistrationAgency","affiliation":[]},{"nameType":"Organizational","name":"UNSW Sydney","nameIdentifiers":[{"nameIdentifierScheme":"ROR","schemeUri":null,"nameIdentifier":"https://ror.org/03r8z3t63"}],"contributorType":"HostingInstitution","affiliation":[]}],"dates":[{"date":"2022-07-01/2026-12-31","dateType":"Other"}],"language":null,"types":{"schemaOrg":"Project","resourceTypeGeneral":"Project","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":"RAiD"},"relatedIdentifiers":[{"relationType":"IsReferencedBy","resourceTypeGeneral":"Report","relatedIdentifier":"https://doi.org/10.26190/unsworks/31270","relatedIdentifierType":"DOI"},{"relationType":"IsReferencedBy","resourceTypeGeneral":"Report","relatedIdentifier":"https://doi.org/10.26190/unsworks/31284","relatedIdentifierType":"DOI"},{"relationType":"IsReferencedBy","resourceTypeGeneral":"Dataset","relatedIdentifier":"https://doi.org/10.26190/unsworks/32211","relatedIdentifierType":"DOI"},{"relationType":"IsReferencedBy","resourceTypeGeneral":"BookChapter","relatedIdentifier":"https://doi.org/10.1016/B978-0-443-45248-2.00026-2","relatedIdentifierType":"DOI"},{"relationType":"IsDocumentedBy","resourceTypeGeneral":"Other","relatedIdentifier":"https://www.nespsustainable.edu.au/research/impact-priority-2-plastic-and-waste-materials#items-229","relatedIdentifierType":"URL"},{"relationType":"IsPartOf","resourceTypeGeneral":"Project","relatedIdentifier":"https://raid.org/10.71821/35f86452","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[],"descriptions":[{"descriptionType":"Abstract","description":"This project seeks to identify and trial fit for purpose technological recycling solutions, utilising hub and spoke models for remote/very remote, inner and outer regional communities across Australia.  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Directing wind into forests does not compromise system efficiency, with onshore curtailment remaining largely unchanged at 3 to 8 percent. Land-use and technology decisions that appear independent may, in fact, interact, as solar deployment reshapes the value of each wind resource and determines which is displaced from the generation mix. By quantifying the ecological value forgone per unit of forest onshore wind, this study makes explicit a trade-off that energy system modelling usually leaves implicit, providing a transferable basis for balancing the competing climate strategies of protecting forests and sustaining wind expansion."}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2607.17959","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-21T03:30:25Z","registered":"2026-07-21T03:30:26Z","published":null,"updated":"2026-07-21T03:30:26Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2607.17952","type":"dois","attributes":{"doi":"10.48550/arxiv.2607.17952","identifiers":[{"identifier":"2607.17952","identifierType":"arXiv"}],"creators":[{"nameType":"Personal","givenName":"Guosheng","familyName":"Li","name":"Li, Guosheng","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Fenghui","familyName":"Ren","name":"Ren, Fenghui","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Bin","familyName":"Liu","name":"Liu, Bin","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Chuan","familyName":"Yu","name":"Yu, Chuan","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Kaiying","familyName":"Ji","name":"Ji, Kaiying","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Lin","familyName":"Yue","name":"Yue, Lin","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Jun","familyName":"Shen","name":"Shen, Jun","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Sasa","familyName":"Qian","name":"Qian, Sasa","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"What Transfers Under Source Shift? 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Existing evaluations are mostly conducted within a single source, leaving open whether common LLM adaptation strategies remain effective under source shift. We reframe climate disclosure classification as a cross-source adaptation problem and study three widely used adaptation strategies -- definitions, examples, and fine-tuning -- across eleven open- and closed-source LLMs, using two corpora that share the same label space but come from different sources. We find that all strategies bring positive cross-source gains on average, but the strongest in-source strategies are not the strongest cross-source ones: similarity-based retrieval and LoRA fine-tuning gain most in-source but lose most of that advantage under source shift; randomly selected few-shot examples, a weaker in-source baseline, retain their advantage more reliably; definitions transfer most consistently, though only when their granularity matches the target text. 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Licensed under CC BY 4.0."}],"descriptions":[{"descriptionType":"Abstract","description":"One-minute-resolution field dataset and analysis comparing five 20-ft shipping containers of differing colour, ventilation and insulation, plus a radiation-shielded external reference, under shared subtropical exposure in Brisbane, Australia, over eight days in November 2025. The package contains the raw per-logger CSV files (Govee H5179), a documented Python analysis script, per-day metrics, a flat container summary, a bootstrap confidence-interval table, a data dictionary, and a methods and assumptions document. The observation day is the unit of analysis (n = 6 full days); minute samples are not treated as independent. Headline result: dark steel units run about 8 to 9 K hotter than the cream unit at peak (day-blocked bootstrap 95 percent confidence interval excludes zero), the two dark units are not distinguishable at peak, and the insulated reefer stays coolest and most stable on every day."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21466036","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":1,"created":"2026-07-21T03:29:33Z","registered":"2026-07-21T03:29:34Z","published":null,"updated":"2026-07-21T03:29:34Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.21466035","type":"dois","attributes":{"doi":"10.5281/zenodo.21466035","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Containerbase Pty Ltd, Hemmant QLD, Australia"],"givenName":"Andreas","familyName":"Atrott","name":"Atrott, Andreas","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","nameIdentifier":"0009-0007-9305-8058"}]}],"titles":[{"title":"Passive Thermal and Humidity Behaviour of Steel and Insulated Shipping Containers in a Subtropical Climate: Multi-Container Field Dataset (Brisbane, 04-11 November 2025)"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"shipping containers"},{"subject":"thermal performance"},{"subject":"albedo"},{"subject":"ventilation"},{"subject":"relative humidity"},{"subject":"absolute humidity"},{"subject":"subtropical climate"},{"subject":"field study"},{"subject":"passive cooling"},{"subject":"building envelope"}],"contributors":[],"dates":[{"date":"2026-07-21","dateType":"Issued"},{"date":"2025-11-04/2025-11-11","dateType":"Collected"}],"language":null,"types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":""},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.21466035","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1.0","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"},{"rightsUri":"http://rightsstatements.org/vocab/InC/1.0/","rights":"Copyright (C) 2025 Containerbase Pty Ltd / Andreas Atrott. Licensed under CC BY 4.0."}],"descriptions":[{"descriptionType":"Abstract","description":"One-minute-resolution field dataset and analysis comparing five 20-ft shipping containers of differing colour, ventilation and insulation, plus a radiation-shielded external reference, under shared subtropical exposure in Brisbane, Australia, over eight days in November 2025. The package contains the raw per-logger CSV files (Govee H5179), a documented Python analysis script, per-day metrics, a flat container summary, a bootstrap confidence-interval table, a data dictionary, and a methods and assumptions document. The observation day is the unit of analysis (n = 6 full days); minute samples are not treated as independent. Headline result: dark steel units run about 8 to 9 K hotter than the cream unit at peak (day-blocked bootstrap 95 percent confidence interval excludes zero), the two dark units are not distinguishable at peak, and the insulated reefer stays coolest and most stable on every day."}],"geoLocations":[],"fundingReferences":[],"url":"https://zenodo.org/doi/10.5281/zenodo.21466035","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":"2026-07-21T03:29:33Z","registered":"2026-07-21T03:29:34Z","published":null,"updated":"2026-07-21T03:29:34Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.48550/arxiv.2607.17511","type":"dois","attributes":{"doi":"10.48550/arxiv.2607.17511","identifiers":[{"identifier":"2607.17511","identifierType":"arXiv"}],"creators":[{"nameType":"Personal","givenName":"Wentao","familyName":"Gao","name":"Gao, Wentao","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Jiuyong","familyName":"Li","name":"Li, Jiuyong","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Lin","familyName":"Liu","name":"Liu, Lin","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Thuc Duy","familyName":"Le","name":"Le, Thuc Duy","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Jixue","familyName":"Liu","name":"Liu, Jixue","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Yanchang","familyName":"Zhao","name":"Zhao, Yanchang","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Yun","familyName":"Chen","name":"Chen, Yun","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Lightweight Wrappers for Adapting Time Series Foundation Models to Regional Drought Forecasting"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"subject":"Machine Learning (cs.LG)","lang":"en","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-07-20T03:36:15Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-07-21T01:13:31Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-07","dateType":"Available","dateInformation":"v1"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":"Article"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"1","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Large \\emph{Time Series Foundation Models} (TSFMs) demonstrate strong zero-shot forecasting capabilities across diverse domains. However, their application to regional climate forecasting faces practical challenges: model weights are often proprietary, local training records are limited, and computational budgets are constrained, making traditional fine-tuning approaches infeasible. To address these constraints, we introduce a lightweight, black-box adaptation framework (requiring no access to backbone parameters and no backbone fine-tuning) that enhances frozen TSFMs at inference time through two plug-and-play wrappers: \\textbf{SMR\\textsuperscript{2}} (Stationarity aware multi-resolution Residual), which decomposes the input into multi-resolution temporal views, learns stride specific residual corrections that capture regional dynamics, then adaptively ensembles them into a single forecast, and \\textbf{MBB} (Moving Block Bootstrap), which preserves temporal dependencies through block resampling and ensembles over temporally coherent residual perturbations to stabilize the point forecast. Both wrappers instantiate the same bagging style principle: they build diverse views of the input or its residuals, forecast each with the same frozen backbone, and aggregate, so all adaptation comes from inference time ensembling rather than any weight update. Evaluated on one month ahead Standardized Precipitation Evapotranspiration Index (SPEI) prediction across multiple sites in South Australia, our framework consistently improves forecasting performance across several backbone models, demonstrating up to 26\\% mean squared error (MSE) reduction over the corresponding frozen backbone while enabling practical deployment in resource constrained regional forecasting systems."},{"descriptionType":"Other","description":"12 Pages, KDD2026 Accpeted Paper(Poster)"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2607.17511","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-21T03:20:45Z","registered":"2026-07-21T03:20:46Z","published":null,"updated":"2026-07-21T03:20:46Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2607.17507","type":"dois","attributes":{"doi":"10.48550/arxiv.2607.17507","identifiers":[{"identifier":"2607.17507","identifierType":"arXiv"}],"creators":[{"nameType":"Personal","givenName":"Wentao","familyName":"Gao","name":"Gao, Wentao","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Jiuyong","familyName":"Li","name":"Li, Jiuyong","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Lin","familyName":"Liu","name":"Liu, Lin","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Thuc Duy","familyName":"Le","name":"Le, Thuc Duy","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Jixue","familyName":"Liu","name":"Liu, Jixue","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Yanchang","familyName":"Zhao","name":"Zhao, Yanchang","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Yun","familyName":"Chen","name":"Chen, Yun","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Residual-Guided Multi-Resolution Refinement of Foundation Models: A Case Study in Drought Forecasting"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"subject":"Machine Learning (cs.LG)","lang":"en","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-07-20T03:29:30Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-07-21T01:13:18Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-07","dateType":"Available","dateInformation":"v1"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":"Article"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"1","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Attribution 4.0 International","rightsIdentifier":"cc-by-4.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Regional climate prediction presents unique challenges for time series foundation models, which typically process temporal patterns through single-pass inference. Expert climatologists, in contrast, employ multi-scale temporal analysis and iterative refinement based on systematic error diagnosis. We present RGMR (Residual-Guided Multi-Resolution Refinement), an inference-time framework that adapts pre-trained foundation models to perform structured coarse-to-fine refinement for climate forecasting without updating backbone parameters. Applied to drought forecasting using the Standardized Precipitation Evapotranspiration Index (SPEI), RGMR is architecture-agnostic across the three TSFM backbones evaluated per site (TimesFM, TimeGPT, TabPFN) and consistently lowers test-set MSE on three South Australian sites and three additional regions outside South Australia. Applied to TimesFM, the wrapper reduces one-month-ahead SPEI MSE by up to 18.9\\% across the three South Australian sites (mean reduction $\\approx$18.7\\%). Overall, RGMR provides a practical route for deploying frozen TSFMs in regional climate forecasting workflows."},{"descriptionType":"Other","description":"23 pages, ICML2026 Accepted Paper(Poster)"}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2607.17507","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-21T03:20:39Z","registered":"2026-07-21T03:20:39Z","published":null,"updated":"2026-07-21T03:20:39Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}},{"id":"10.48550/arxiv.2607.17024","type":"dois","attributes":{"doi":"10.48550/arxiv.2607.17024","identifiers":[{"identifier":"2607.17024","identifierType":"arXiv"}],"creators":[{"nameType":"Personal","givenName":"Maede Azani Hassan","familyName":"Abadi","name":"Abadi, Maede Azani Hassan","affiliation":[],"nameIdentifiers":[]},{"nameType":"Personal","givenName":"Shouyi","familyName":"Wang","name":"Wang, Shouyi","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Interpretable Machine Learning for Air Pollution and Respiratory Health Prediction: A Socioeconomic Subgroup Analysis"}],"publisher":"arXiv","container":{},"publicationYear":2026,"subjects":[{"subject":"Machine Learning (cs.LG)","lang":"en","subjectScheme":"arXiv"},{"subject":"FOS: Computer and information sciences","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2026-07-19T01:36:30Z","dateType":"Submitted","dateInformation":"v1"},{"date":"2026-07-21T00:45:23Z","dateType":"Updated","dateInformation":"v1"},{"date":"2026-07","dateType":"Available","dateInformation":"v1"}],"language":null,"types":{"schemaOrg":"CreativeWork","resourceTypeGeneral":"Preprint","citeproc":"article","bibtex":"misc","ris":"GEN","resourceType":"Article"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":"1","rightsList":[{"rightsUri":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","rights":"arXiv.org perpetual, non-exclusive license"}],"descriptions":[{"descriptionType":"Abstract","description":"Air pollution and climate-related stressors are increasingly important concerns for respiratory health, especially in settings with unequal environmental exposure and healthcare capacity. This study evaluates an interpretable machine learning framework for predicting respiratory disease rates and air-quality status using structured country-level weekly data. Two supervised learning tasks were considered: regression of respiratory disease rate per 100,000 population and binary classification of air-quality status. Nine regression models and nine classification models were compared using nested cross-validation. Model interpretation was conducted using SHAP values, and subgroup analysis was performed across income levels and geographic regions. The results showed that PM2.5 concentration was the dominant predictor of respiratory disease rate, with linear and regularized linear models achieving the strongest regression performance. For air-quality classification, models achieved high balanced accuracy when PM2.5 was included, but performance decreased substantially when PM2.5 was removed, indicating strong dependence on pollutant-related information. SHAP analysis showed that, without PM2.5, socioeconomic and meteorological variables such as GDP per capita, precipitation, and healthcare access became more influential. Subgroup analysis showed similar aggregate regression error across income groups, but PM2.5 contributed more strongly to predictions in lower-middle-income countries. These results show that model accuracy alone is not sufficient for climate-health prediction. Interpretable models can help identify dominant pollution-related signals, test whether results depend on key pollutant variables, and show whether prediction patterns differ across socioeconomic groups."}],"geoLocations":[],"fundingReferences":[],"url":"https://arxiv.org/abs/2607.17024","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-07-21T03:10:12Z","registered":"2026-07-21T03:10:13Z","published":null,"updated":"2026-07-21T03:10:13Z"},"relationships":{"client":{"data":{"id":"arxiv.content","type":"clients"}}}}],"meta":{"total":583414,"totalPages":400,"page":1},"links":{"self":"https://api.datacite.org/dois?query=climate","next":"https://api.datacite.org/dois?page%5Bnumber%5D=2\u0026page%5Bsize%5D=25\u0026query=climate"}}