{"id":"W2133488945","doi":"10.1136/injuryprev-2014-041358","title":"Did Chile’s traffic law reform push police enforcement? Understanding Chile’s traffic fatalities and injuries reduction","year":2014,"lang":"en","type":"article","venue":"Injury Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; McGill University","funders":"","keywords":"Law enforcement; Enforcement; Poison control; Occupational safety and health; Human factors and ergonomics; Injury prevention; Criminology; Suicide prevention; Forensic engineering; Traffic police; Road traffic; Political science; Law; Engineering; Transport engineering; Computer security; Medical emergency; Medicine; Psychology; Computer science","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":[0.001609519,0.0001744134,0.0002040899,0.001371667,0.0006459989,0.002687708,0.0006705032,0.0007265761,0.004611623],"category_scores_gemma":[0.004861559,0.0001710311,0.0002909894,0.001647638,0.001654979,0.002322323,0.001644118,0.001279552,0.0002525253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006920591,"about_ca_system_score_gemma":0.007425476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1072315,"about_ca_topic_score_gemma":0.08378209,"domain_scores_codex":[0.9992,0.0002314878,0.0000319297,0.0001264317,0.000163215,0.0002469913],"domain_scores_gemma":[0.9975252,0.0006416705,0.001049852,0.0001018563,0.000442519,0.0002389736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001173039,0.0004631171,0.7217619,0.001765523,0.0002502773,0.00092489,0.03795643,0.004158696,0.001245657,0.08058371,0.02215585,0.1286166],"study_design_scores_gemma":[0.00001424213,0.00008234402,0.8935847,0.001169152,0.00005096714,0.00009204345,0.03325705,0.001814352,0.0002736294,0.008738168,0.06088762,0.00003564917],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8375068,0.01149272,0.001908794,0.08966776,0.0001780345,0.00006774248,0.001771858,0.00003264084,0.05737365],"genre_scores_gemma":[0.990973,0.004994569,0.000322157,0.0009669674,0.000110285,0.00004034848,0.0003022465,0.000006317195,0.002283965],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1072315,"threshold_uncertainty_score":0.2132147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01426753503088442,"score_gpt":0.2337479458470527,"score_spread":0.2194804108161683,"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."}}