{"id":"W4391226303","doi":"10.1007/s11356-024-31969-z","title":"Revisiting the importance of temperature, weather and air pollution variables in heat-mortality relationships with machine learning","year":2024,"lang":"en","type":"article","venue":"Environmental Science and Pollution Research","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de Santé Publique du Québec; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Institut National de Santé Publique du Québec","keywords":"Context (archaeology); Environmental science; Air temperature; Index (typography); Metropolitan area; Apparent temperature; Urban heat island; Statistical model; Linear model; Predictive modelling; Air pollution; Mean radiant temperature; Meteorology; Climate change; Econometrics; Statistics; Geography; Mathematics; Computer science; Ecology; Humidity","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01494714,0.0009521931,0.0008371366,0.001540463,0.000821504,0.003740996,0.002021783,0.0008843776,0.003656365],"category_scores_gemma":[0.0404006,0.0003796082,0.00150394,0.00187999,0.001470909,0.003042357,0.001253232,0.003174748,0.0004888025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006883361,"about_ca_system_score_gemma":0.00202252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01635678,"about_ca_topic_score_gemma":0.02154726,"domain_scores_codex":[0.995806,0.00299636,0.0002319114,0.000457108,0.0003007107,0.0002078591],"domain_scores_gemma":[0.9333488,0.06091168,0.001653207,0.001603698,0.001888093,0.0005944678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007963019,0.0006460089,0.7389445,0.0007236426,0.003822706,0.0006416122,0.0009581035,0.07117672,0.002426431,0.01516271,0.003448762,0.1612524],"study_design_scores_gemma":[0.0001308002,0.0004181791,0.375908,0.0005510444,0.001759317,0.000243081,0.001401516,0.5452433,0.002402263,0.06587175,0.005963921,0.0001068504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9212691,0.01312017,0.04341584,0.01199987,0.0005665957,0.00008107452,0.0004253417,0.0001246035,0.008997475],"genre_scores_gemma":[0.9925404,0.001224921,0.004050228,0.0003781979,0.0003628765,0.00001071318,0.0001563769,0.00002226824,0.001253983],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01635678,"threshold_uncertainty_score":0.07904893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07473631060409228,"score_gpt":0.3529510164012433,"score_spread":0.278214705797151,"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."}}