{"id":"W4407729722","doi":"10.2139/ssrn.5138135","title":"Associations between All-Cause and Ischemic Heart Disease Mortality and Long-Term Ambient Ultrafine Particles Exposure: A Comparison of Statistical and Machine Learning Exposure Models","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Air Quality and Health Impacts","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Government of Canada; Health Canada; Université de Montréal","funders":"","keywords":"Disease; Term (time); Cardiology; Medicine; Statistical learning; Internal medicine; Artificial intelligence; Computer science; Physics","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":[0.009977017,0.0007891092,0.0008467473,0.0008907805,0.0002861297,0.001519336,0.001205639,0.001303385,0.002593277],"category_scores_gemma":[0.0158044,0.0003060913,0.002333667,0.0008660532,0.000432189,0.001002648,0.000689244,0.001210778,0.0004713565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005086021,"about_ca_system_score_gemma":0.0008809927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007936872,"about_ca_topic_score_gemma":0.003762767,"domain_scores_codex":[0.9981191,0.001156462,0.00008251181,0.0004029007,0.0001471176,0.00009201269],"domain_scores_gemma":[0.9784062,0.01898192,0.0008215419,0.001016868,0.0005443124,0.0002291414],"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":[0.008943892,0.001540499,0.7069334,0.0004080655,0.009076563,0.0002632609,0.0004488267,0.1814551,0.00198555,0.003966214,0.003211895,0.08176666],"study_design_scores_gemma":[0.0004204339,0.001508109,0.3436764,0.00007052653,0.002315069,0.0002346176,0.0003788886,0.6386544,0.0007749625,0.01045741,0.001420543,0.00008857159],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.973416,0.001241716,0.02161157,0.0008010888,0.0001320571,0.00003759881,0.001565165,0.0002107625,0.0009840729],"genre_scores_gemma":[0.9918867,0.0004752345,0.004104038,0.000103563,0.0001285462,0.00003442472,0.002127975,0.00006822378,0.001071273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009977017,"threshold_uncertainty_score":0.05276412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08395448319799519,"score_gpt":0.3656574863801646,"score_spread":0.2817030031821694,"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."}}