{"id":"W4388972674","doi":"10.1029/2023ef003506","title":"Nonstationarity in High and Low‐Temperature Extremes: Insights From a Global Observational Data Set by Merging Extreme‐Value Methods","year":2023,"lang":"en","type":"article","venue":"Earth s Future","topic":"Climate variability and models","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Global Institute for Water Security; University of Calgary; University of Saskatchewan","funders":"Global Water Futures","keywords":"Merge (version control); Extreme value theory; Climatology; Environmental science; Econometrics; Statistics; Mathematics; Computer science; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004380627,0.00016935,0.000183789,0.00002547326,0.0001609047,0.00006526376,0.0003107391,0.0001693917,0.0006482669],"category_scores_gemma":[0.00009353468,0.0001542823,0.00002270644,0.0006189446,0.00009535558,0.0006015019,0.0004715587,0.0002231266,0.00005951495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005468053,"about_ca_system_score_gemma":0.00003066982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001738796,"about_ca_topic_score_gemma":0.002518812,"domain_scores_codex":[0.9983209,0.000238203,0.0002387244,0.0006446088,0.0003248035,0.0002327829],"domain_scores_gemma":[0.9991524,0.0001994586,0.0000543834,0.0004852139,0.000008916735,0.00009961743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002714865,0.0006171438,0.6137322,0.0001497896,0.0001529626,0.0001423162,0.01007032,0.04246612,0.1603061,0.01828582,0.125244,0.02856176],"study_design_scores_gemma":[0.000708441,0.00001549507,0.8532424,0.00003913679,0.00001718798,0.000002947335,0.0005752362,0.08271396,0.0001613867,0.01668014,0.04551331,0.0003303704],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938996,0.0002884836,0.000411496,0.002505432,0.0002791622,0.0001973261,0.002020695,0.0000693749,0.00032842],"genre_scores_gemma":[0.9091544,0.000345883,0.07847638,0.001530055,0.0002620329,0.00002659082,0.009948133,0.0000230185,0.0002334753],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2395102,"threshold_uncertainty_score":0.7098069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07141654077207209,"score_gpt":0.3184172259017288,"score_spread":0.2470006851296567,"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."}}