{"id":"W2946645102","doi":"10.1177/0361198119850459","title":"Does Automated Enforcement Presence Impact Collisions and Crime?","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":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Software deployment; Law enforcement; Enforcement; Collision; Computer security; Visibility; Transport engineering; Computer science; Business; Engineering; Geography; Political science; Law","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":[0.001694247,0.0002797124,0.000418046,0.001389042,0.0009085356,0.002729478,0.001038696,0.001202459,0.00795716],"category_scores_gemma":[0.01524878,0.0004445443,0.0007989127,0.001272349,0.001252048,0.001778089,0.001448811,0.0008614529,0.0005777224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001526575,"about_ca_system_score_gemma":0.002134299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03720587,"about_ca_topic_score_gemma":0.04729661,"domain_scores_codex":[0.9970869,0.001261348,0.0001216877,0.0003475788,0.0005463999,0.0006359632],"domain_scores_gemma":[0.985889,0.006315107,0.004793657,0.0007147063,0.001227758,0.001059727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002643628,0.0006256996,0.975778,0.00006046355,0.0002631925,0.0001262874,0.0004461822,0.001226425,0.0001576117,0.0009891974,0.0006692411,0.01939344],"study_design_scores_gemma":[0.00001262899,0.000185491,0.9953402,0.00004984359,0.00008061701,0.00006853043,0.001353619,0.001545649,0.00009493788,0.000482648,0.0007756763,0.00001014395],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922925,0.0004364161,0.0003555966,0.001355096,0.00002917426,0.00002312187,0.0001463947,0.00001401312,0.005347523],"genre_scores_gemma":[0.9987723,0.0002345939,0.000150267,0.00007489494,0.00002500729,0.000006770387,0.00006435693,0.000005463242,0.0006663331],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03720587,"threshold_uncertainty_score":0.0739786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03484306735859801,"score_gpt":0.3572575311367341,"score_spread":0.3224144637781361,"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."}}