{"id":"W4414965358","doi":"10.1136/oemed-2025-epicohabstracts.273","title":"8287721 Extreme weather events caused by climate change: estimating the prevalence of at-risk workers","year":2025,"lang":"en","type":"article","venue":"","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Extreme weather; Extreme heat; Climate change; Hazard; Population; Risk assessment; Population health; Climate risk","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001476556,0.0003970571,0.0004680634,0.005294024,0.001221292,0.001202287,0.00120828,0.000534431,0.004935514],"category_scores_gemma":[0.008060787,0.0003175091,0.001357306,0.007624305,0.0003060003,0.0004079422,0.0007739143,0.0004934234,0.0008981131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00731897,"about_ca_system_score_gemma":0.0214373,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.9430755,"about_ca_topic_score_gemma":0.9575689,"domain_scores_codex":[0.9986302,0.0001432765,0.0002239118,0.0001580298,0.0006629968,0.0001816374],"domain_scores_gemma":[0.9950713,0.0006355298,0.001103224,0.0001021171,0.002672299,0.0004155872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009814678,0.00003457481,0.9473008,0.002932066,0.0004028745,0.0001103372,0.0005212005,0.0003148692,0.0001487536,0.0001924616,0.02121187,0.02673212],"study_design_scores_gemma":[0.00001516358,0.00002080664,0.9888558,0.001186958,0.0003002284,0.00009391054,0.0008735633,0.0003571886,0.0001018897,0.00006235409,0.008118535,0.00001345803],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6017442,0.04694808,0.005003146,0.003743249,0.0003355033,0.002677677,0.3019016,0.0002769359,0.03736966],"genre_scores_gemma":[0.8534209,0.03037405,0.01374096,0.001660684,0.0001610547,0.002973309,0.09023683,0.00005360399,0.007378564],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9430755,"threshold_uncertainty_score":0.1145194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06559279346423015,"score_gpt":0.320520150776224,"score_spread":0.2549273573119938,"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."}}