{"id":"W4408149471","doi":"10.1097/ee9.0000000000000375","title":"Do we need flexible machine-learning algorithms to assess the effect of long-term exposure to fine particulate matter on mortality?: An example from a Canadian national cohort","year":2025,"lang":"en","type":"article","venue":"Environmental Epidemiology","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute for Clinical Evaluative Sciences; Public Health Ontario; University of Toronto; Health Canada; McGill University","funders":"National Institute on Aging; Health Canada; Government of Canada","keywords":"Particulates; Term (time); Cohort; Computer science; Machine learning; Algorithm; Artificial intelligence; Environmental science; Statistics; Mathematics; Physics; Ecology; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002832552,0.0002585573,0.0005143429,0.00008937562,0.000309305,0.00001358743,0.0003713116,0.0001660906,0.004946324],"category_scores_gemma":[0.0002657919,0.000194171,0.00008364348,0.0001739224,0.0002090152,0.0001068785,0.0002208155,0.0003494293,0.0009215478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006287668,"about_ca_system_score_gemma":0.00002638066,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1376927,"about_ca_topic_score_gemma":0.02784246,"domain_scores_codex":[0.9963583,0.001524909,0.0005904597,0.0006115302,0.0002897496,0.0006250356],"domain_scores_gemma":[0.9974825,0.001355609,0.0001462791,0.000459359,0.000002250201,0.00055395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008540737,0.00004705058,0.9629258,0.00000949784,0.00005001004,0.000005103504,0.0003208206,0.0289078,0.0005178312,0.00005889119,0.002367367,0.00470445],"study_design_scores_gemma":[0.0003636785,0.0006614227,0.9925861,0.00003386123,0.00003382591,0.000002945873,0.00003770764,0.001342714,0.001187675,0.0002926592,0.003280322,0.0001770626],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878005,0.00008352326,0.001192181,0.009176148,0.0001473354,0.0007934084,0.0001131739,0.0000214879,0.0006722587],"genre_scores_gemma":[0.9850665,0.00002213244,0.0005434767,0.01354677,0.00005629569,0.0001097876,0.0001853285,0.00001860783,0.0004511513],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1098503,"threshold_uncertainty_score":0.9998564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07719960887373632,"score_gpt":0.3612184306233205,"score_spread":0.2840188217495842,"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."}}