{"id":"W2945253341","doi":"10.1109/bigdata47090.2019.9006009","title":"High-Resolution Road Vehicle Collision Prediction for the City of Montreal","year":2019,"lang":"en","type":"article","venue":"","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Random forest; Context (archaeology); Leverage (statistics); Collision; Predictive modelling; Computer science; Road accident; Accident (philosophy); Artificial intelligence; Machine learning; Transport engineering; Computer security; Geography; Engineering","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":[0.0004615409,0.00178623,0.0004405949,0.001365972,0.0009013618,0.0007859268,0.00149353,0.0007943211,0.00267265],"category_scores_gemma":[0.00159541,0.0003444193,0.0008548684,0.001445247,0.0003065372,0.000581457,0.0006428678,0.0008911139,0.000835232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00517191,"about_ca_system_score_gemma":0.004538714,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9144334,"about_ca_topic_score_gemma":0.9280701,"domain_scores_codex":[0.9996694,0.00003349907,0.00001126121,0.0001045746,0.00007241832,0.0001088571],"domain_scores_gemma":[0.9994343,0.00009240099,0.00004107596,0.00006041628,0.0002785857,0.00009322877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009240626,0.000815664,0.3492707,0.0003184377,0.0006431168,0.001636103,0.0002413444,0.437033,0.004096413,0.001345633,0.1059114,0.097764],"study_design_scores_gemma":[0.00006640653,0.00005916274,0.08436529,0.00002946291,0.00005905125,0.00006283275,0.0002975061,0.905897,0.001599238,0.0004744659,0.007043431,0.00004628218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8988384,0.001155248,0.01186542,0.001474323,0.0002568603,0.000176011,0.0751704,0.006263208,0.004800204],"genre_scores_gemma":[0.8842366,0.0003803821,0.01232237,0.0001392782,0.00005002711,0.00005400251,0.09936307,0.0001502654,0.003303935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08556658,"threshold_uncertainty_score":0.172141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007122523971082708,"score_gpt":0.1960524098095045,"score_spread":0.1889298858384218,"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."}}