{"id":"W2901722539","doi":"10.1111/ecin.12739","title":"WHAT DO BICYCLE HELMET LAWS DO? EVIDENCE FROM CANADA","year":2018,"lang":"en","type":"article","venue":"Economic Inquiry","topic":"Injury Epidemiology and Prevention","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Unintended consequences; Population; Injury prevention; Law; Suicide prevention; Human factors and ergonomics; Current Population Survey; Affect (linguistics); Poison control; Cycling; Demographic economics; Psychology; Political science; Demography; Economics; Medicine; Environmental health; Sociology; History","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006232843,0.0001202951,0.000288306,0.00004029525,0.0001012494,0.00002688125,0.0001208645,0.0001320771,0.003607835],"category_scores_gemma":[0.0001588253,0.0001142595,0.00006423266,0.00002953439,0.0001903591,0.000398778,0.00005116596,0.000157303,0.001105631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002841334,"about_ca_system_score_gemma":0.0004248501,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1397924,"about_ca_topic_score_gemma":0.1538547,"domain_scores_codex":[0.9989263,0.0001256031,0.0003224129,0.0003360414,0.00004791728,0.0002416588],"domain_scores_gemma":[0.9990156,0.0003103466,0.0001186828,0.0004246308,0.00002596682,0.0001047448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0007748029,0.00004341371,0.2259399,0.00004873343,0.0003017275,0.00004913049,0.00236506,0.00001888424,0.001793051,0.0008021115,0.7308815,0.03698179],"study_design_scores_gemma":[0.001838066,0.0007962487,0.6573743,0.001821086,0.0002865697,0.00009467053,0.002676862,0.0006221132,0.02699426,0.01309161,0.2936202,0.0007839843],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.981532,0.002456083,0.00007442063,0.002660919,0.01167849,0.0001580654,0.00001498216,0.00002709944,0.001397878],"genre_scores_gemma":[0.989077,0.0005404387,0.0003873734,0.003714482,0.005031493,0.00001197142,0.00003725238,0.00001382191,0.001186168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4372612,"threshold_uncertainty_score":0.9996721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0594313788062273,"score_gpt":0.3443936804244966,"score_spread":0.2849623016182693,"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."}}