{"id":"W4368362043","doi":"10.1111/dar.13678","title":"Did the cannabis recreational use law affect traffic crash outcomes in Toronto? Building evidence for the adequate number of authorised cannabis stores' thresholds","year":2023,"lang":"en","type":"article","venue":"Drug and Alcohol Review","topic":"Cannabis and Cannabinoid Research","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Cannabis; Per capita; Confidence interval; Crash; Affect (linguistics); Poison control; Demography; Injury prevention; Medicine; Geography; Environmental health; Psychology; Psychiatry; Computer science; Population; Sociology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002715685,0.000254549,0.0006968427,0.00005010674,0.0002593482,0.00006725151,0.000283228,0.0000712434,0.0002482941],"category_scores_gemma":[0.001029321,0.0001344579,0.0003471567,0.0005192954,0.0002602096,0.0002290385,0.0001112641,0.0002656661,0.00000775243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002019049,"about_ca_system_score_gemma":0.0002382628,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006952853,"about_ca_topic_score_gemma":0.004204838,"domain_scores_codex":[0.9976932,0.0002372916,0.0005534737,0.0004130082,0.000619698,0.0004833476],"domain_scores_gemma":[0.998385,0.0004944398,0.000152909,0.0005755961,0.0002291795,0.000162883],"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":[0.0003786171,0.0002223925,0.07905414,0.008167326,0.0004875792,0.00004736845,0.002419268,0.0000821368,0.0002792207,0.0160373,0.7797495,0.1130751],"study_design_scores_gemma":[0.002780055,0.0002588127,0.5174018,0.01395504,0.0009275832,0.00007773632,0.001249034,0.00165185,0.0002115786,0.0001888627,0.4606977,0.0005999583],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7053056,0.1507038,0.00001374057,0.1382224,0.0003216915,0.004902101,0.0001260735,0.00007308594,0.0003314967],"genre_scores_gemma":[0.8778076,0.09361932,0.000055442,0.001783671,0.0001789635,0.00158337,0.00003586629,0.00005622565,0.02487954],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4383476,"threshold_uncertainty_score":0.99966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09973441985817978,"score_gpt":0.4371983609443767,"score_spread":0.3374639410861969,"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."}}