{"id":"W2146006075","doi":"10.1136/injuryprev-2012-040351","title":"The challenges of implementing interlock best practices in a federal state: the Canadian experience","year":2012,"lang":"en","type":"article","venue":"Injury Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Interlock; Legislature; Government (linguistics); Countermeasure; State (computer science); Computer security; Law; Business; Federal law; Drunk drivers; Poison control; Engineering; Drunk driving; Legislation; Injury prevention; Political science; Medicine; Medical emergency; Computer science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.07191889,0.000861116,0.001285454,0.004892298,0.05141653,0.02084036,0.01083194,0.00990974,0.01070879],"category_scores_gemma":[0.08252653,0.001478097,0.001420944,0.00915097,0.02146062,0.008049143,0.01591452,0.01472925,0.0008821183],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.3342468,"about_ca_system_score_gemma":0.6943041,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9929727,"about_ca_topic_score_gemma":0.9958332,"domain_scores_codex":[0.9356698,0.0158079,0.00364655,0.004074562,0.02598758,0.01481358],"domain_scores_gemma":[0.7937506,0.03417186,0.005228384,0.006744334,0.09870931,0.06139554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003090242,0.001232726,0.0284871,0.002360963,0.0002123048,0.00355495,0.1909997,0.002921118,0.002082147,0.226948,0.2275112,0.3133808],"study_design_scores_gemma":[0.0001073925,0.0002917983,0.04028679,0.004479434,0.0001078015,0.0005297554,0.1584733,0.002087699,0.0007824865,0.01594917,0.7763125,0.0005917801],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.08767393,0.01907333,0.005216267,0.7657661,0.003017611,0.0009153389,0.0008722466,0.0003359762,0.1171293],"genre_scores_gemma":[0.8003982,0.02847803,0.03350269,0.1007351,0.00051766,0.0004852898,0.0008740984,0.0004787851,0.03453016],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3342468,"threshold_uncertainty_score":0.7721794,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04512187417149426,"score_gpt":0.3288707860403737,"score_spread":0.2837489118688794,"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."}}