{"id":"W2002464155","doi":"10.1016/j.aap.2010.11.022","title":"Should traffic enforcement be unpredictable? The case of red light cameras in Edmonton","year":2010,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Centre for Transportation Engineering and Planning; Alberta Motor Association Foundation for Traffic Safety; Transport Canada","keywords":"Enforcement; Red light; Software deployment; Law enforcement; Transport engineering; Computer security; Punishment (psychology); Computer science; Engineering; Aeronautics; Simulation; Law; Psychology; Political science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00305634,0.0003149024,0.0002999367,0.0006901638,0.0196445,0.004552679,0.00226328,0.008411403,0.007421401],"category_scores_gemma":[0.005475529,0.0006760458,0.0004557308,0.0006555601,0.004204268,0.002232797,0.003256457,0.01350106,0.0002844274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0117891,"about_ca_system_score_gemma":0.01770898,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6026044,"about_ca_topic_score_gemma":0.8783203,"domain_scores_codex":[0.9979846,0.0003884197,0.00003288123,0.0001688541,0.0002380038,0.001187132],"domain_scores_gemma":[0.9970733,0.001166154,0.0002807461,0.0001248199,0.0004092192,0.0009457531],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0005947143,0.001198183,0.1625246,0.0002225457,0.0002060476,0.198654,0.2382344,0.005171303,0.003364804,0.1820377,0.1136053,0.0941864],"study_design_scores_gemma":[0.0000914668,0.0002615837,0.1843854,0.0007738892,0.0001397525,0.01598229,0.5242369,0.004099162,0.001447084,0.01806377,0.2502607,0.0002580234],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.792289,0.000893076,0.0009814173,0.1250144,0.0005129082,0.00005985713,0.00005950398,0.00002669066,0.08016316],"genre_scores_gemma":[0.9546493,0.0007332191,0.0005161547,0.01758035,0.0001672565,0.00002006765,0.00002062316,0.00003065902,0.02628241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3973956,"threshold_uncertainty_score":0.7994718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01123525202901603,"score_gpt":0.2552745810138247,"score_spread":0.2440393289848087,"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."}}