{"id":"W2048163619","doi":"10.1016/j.jsr.2011.08.004","title":"Analysis of precipitation-related motor vehicle collision and injury risk using insurance and police record information for Winnipeg, Canada","year":2011,"lang":"en","type":"article","venue":"Journal of Safety Research","topic":"Traffic and Road Safety","field":"Engineering","cited_by":54,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Impact","funders":"Health Canada","keywords":"Relative risk; Injury prevention; Poison control; Occupational safety and health; Suicide prevention; Human factors and ergonomics; Collision; Risk assessment; Medicine; Environmental health; Medical emergency; Demography; Computer security; Confidence interval; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001099303,0.0005382808,0.0005692144,0.003594516,0.002077538,0.00164513,0.001722588,0.0003548564,0.002402725],"category_scores_gemma":[0.004733998,0.0005500566,0.0008848959,0.006523705,0.000412181,0.000481911,0.001263527,0.0006180035,0.0003015677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02307696,"about_ca_system_score_gemma":0.03445704,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9983425,"about_ca_topic_score_gemma":0.998839,"domain_scores_codex":[0.9988444,0.000185409,0.0001136842,0.0001504347,0.0003478235,0.000358228],"domain_scores_gemma":[0.9970764,0.000240743,0.0003838807,0.00008167007,0.001726274,0.0004910948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002357042,0.0000515771,0.9879124,0.00006894213,0.0002273779,0.0001800614,0.0006791725,0.0007550378,0.0002408383,0.0002522508,0.002075893,0.007320768],"study_design_scores_gemma":[0.00002585292,0.00002474844,0.9951572,0.00003541448,0.00007312754,0.00005709068,0.001475479,0.001432943,0.0001136094,0.00001978467,0.001570617,0.00001428852],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9857284,0.0007339467,0.0003742793,0.0002861533,0.00001612854,0.0001874648,0.0110653,0.00002244645,0.001585881],"genre_scores_gemma":[0.9886287,0.0005739365,0.001127399,0.00006460265,0.000006313777,0.00008441346,0.006837449,0.00002002893,0.002657126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02307696,"threshold_uncertainty_score":0.1674359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02784110111517603,"score_gpt":0.2811829544095943,"score_spread":0.2533418532944183,"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."}}