{"id":"W2948135720","doi":"10.1177/0361198119851447","title":"Use of Objective Safety Evidence to Deploy Automated Enforcement Resources","year":2019,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Software deployment; Enforcement; Transport engineering; Macro; Collision; Speed limit; Law enforcement; Work (physics); Computer science; Business; Computer security; Risk analysis (engineering); Engineering","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":[0.02011656,0.00145944,0.0008695426,0.007169173,0.0007123862,0.004701259,0.002173359,0.001602959,0.006106255],"category_scores_gemma":[0.149794,0.0007840183,0.0007672021,0.003393034,0.001129196,0.006448374,0.00198042,0.001674903,0.0008501205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003960394,"about_ca_system_score_gemma":0.006243702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01470516,"about_ca_topic_score_gemma":0.03036639,"domain_scores_codex":[0.9788534,0.01158674,0.001403466,0.001388336,0.006235407,0.0005328001],"domain_scores_gemma":[0.8266664,0.1116063,0.0318602,0.008917321,0.01947751,0.001472294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001199573,0.003871004,0.3751039,0.002686763,0.001308818,0.0005095963,0.001067819,0.2443146,0.00252201,0.03963121,0.01024648,0.3175383],"study_design_scores_gemma":[0.0006369029,0.003461523,0.123233,0.003999657,0.0007641428,0.0002023945,0.003123939,0.7930617,0.008332149,0.04304157,0.01981744,0.0003256864],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6598026,0.004112017,0.2099303,0.006298546,0.0006161492,0.002965636,0.008028994,0.001241185,0.1070046],"genre_scores_gemma":[0.9432769,0.001193864,0.05119869,0.0002521219,0.00004898044,0.000468617,0.001177861,0.00005522318,0.002327711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02011656,"threshold_uncertainty_score":0.1063878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0827020955474267,"score_gpt":0.3520285847055327,"score_spread":0.269326489158106,"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."}}