{"id":"W4307810401","doi":"10.1177/03611981221128812","title":"Traffic Conflict Prediction at Signal Cycle Level Using Bayesian Optimized Machine Learning Approaches","year":2022,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic and Road Safety","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Platoon; Support vector machine; Computer science; Bayesian probability; Hyperparameter; Machine learning; Random forest; Artificial intelligence; Bayes' theorem; Sensitivity (control systems); Naive Bayes classifier; Engineering; Control (management)","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":[0.001491894,0.0008353613,0.0007965003,0.00109509,0.0002320937,0.0007571923,0.001052609,0.0006395016,0.0007138167],"category_scores_gemma":[0.003490551,0.0004297923,0.0007202306,0.0007176819,0.00029084,0.0008820237,0.0005085461,0.0009498771,0.0002324433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008994987,"about_ca_system_score_gemma":0.001286038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01974884,"about_ca_topic_score_gemma":0.01337691,"domain_scores_codex":[0.9993068,0.0002954727,0.0000370603,0.0001352748,0.0001361737,0.00008928277],"domain_scores_gemma":[0.9986457,0.0007657052,0.0002190531,0.00005933505,0.0002650429,0.00004517056],"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":[0.00003415359,0.00004953945,0.003615998,0.00001016042,0.00002701849,0.00001463807,0.000009905348,0.9823793,0.0002031362,0.0006089408,0.000197048,0.01285024],"study_design_scores_gemma":[0.000001422332,0.000006929879,0.0004263207,0.000001368122,0.000001765208,0.000001250801,0.000002006454,0.9991956,0.00004271442,0.0002872628,0.00003157061,0.000001791999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3738619,0.0005643112,0.6214305,0.0003895614,0.00003819215,0.00007444509,0.0005069561,0.0006346973,0.002499412],"genre_scores_gemma":[0.9672582,0.0001502763,0.03071207,0.00005765206,0.00002089705,0.00006262024,0.000732233,0.00002067771,0.0009852811],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01974884,"threshold_uncertainty_score":0.03926778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1419443828927354,"score_gpt":0.3212603366881647,"score_spread":0.1793159537954294,"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."}}