{"id":"W7092592997","doi":"10.5281/zenodo.17390286","title":"Climate data for adaptation: uncovering real-world use","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Climate Change and Environmental Impact","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Variety (cybernetics); Relevance (law); Climate change; Adaptation (eye); Data quality; Quality (philosophy)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01085205,0.0003293941,0.0003000963,0.003725206,0.002004687,0.004728015,0.0007459021,0.0009765932,0.007230572],"category_scores_gemma":[0.03831929,0.0003901127,0.0004743173,0.0084937,0.004113432,0.008478646,0.006510608,0.001783294,0.0009563367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002910014,"about_ca_system_score_gemma":0.002379951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01073764,"about_ca_topic_score_gemma":0.01322132,"domain_scores_codex":[0.9927421,0.00514178,0.0002624226,0.0006316474,0.0008167065,0.0004054095],"domain_scores_gemma":[0.9726976,0.02153506,0.001518432,0.00206527,0.001730157,0.000453498],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001421246,0.00008602309,0.1133237,0.001610521,0.00008713327,0.0006748978,0.665773,0.0008092964,0.002703707,0.07590721,0.02438737,0.1144951],"study_design_scores_gemma":[0.000009788295,0.00004253894,0.07979451,0.002485375,0.00003451597,0.0004106781,0.6766013,0.003283752,0.001850693,0.04343648,0.1919352,0.0001152238],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8226561,0.008351281,0.05112944,0.03303721,0.0004191628,0.0005209306,0.01925826,0.0002200544,0.06440756],"genre_scores_gemma":[0.978118,0.002813779,0.01134249,0.000871029,0.00005591599,0.0005117361,0.003178909,0.0001981009,0.002909968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01085205,"threshold_uncertainty_score":0.05739182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1305399438302731,"score_gpt":0.3030595303324258,"score_spread":0.1725195865021527,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}