{"id":"W3174900572","doi":"10.1016/j.aap.2021.106269","title":"Validating the Bayesian hierarchical extreme value model for traffic conflict-based crash estimation on freeway segments with site-level factors","year":2021,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China-Yunnan Joint Fund; National Natural Science Foundation of China","keywords":"Extreme value theory; Bayesian probability; Bayesian hierarchical modeling; Crash; Hierarchical database model; Statistics; Generalized extreme value distribution; Block (permutation group theory); Multilevel model; Computer science; Bayesian inference; Econometrics; Mathematics; Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003275908,0.000253319,0.0003172764,0.000200426,0.0002573282,0.0001145222,0.0001728184,0.0001082669,0.00008199632],"category_scores_gemma":[0.00004273891,0.0001880797,0.0004159174,0.0005893684,0.00002278203,0.0001852174,0.00002015365,0.0001896287,0.000006564263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001280842,"about_ca_system_score_gemma":0.00005657316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009460758,"about_ca_topic_score_gemma":0.001013344,"domain_scores_codex":[0.9983861,0.0001126136,0.0004351499,0.0003595811,0.0004229184,0.0002836127],"domain_scores_gemma":[0.9991262,0.0001959068,0.0001210742,0.0003927594,0.00008151505,0.00008256688],"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":[0.00002166966,0.00008807629,0.01104295,0.00001063072,0.000703252,0.000001557466,0.0004054025,0.980531,0.0003285855,0.0001856548,0.00008524143,0.00659594],"study_design_scores_gemma":[0.0006385171,0.00004133655,0.04473066,0.00005731848,0.001376238,6.50085e-7,0.00009599439,0.9511848,0.001535029,0.0001017929,0.00001668981,0.0002209741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4510048,0.00002506419,0.5484928,0.00007608111,0.00003756791,0.0002081819,0.000004986024,0.0001097554,0.00004073172],"genre_scores_gemma":[0.9829131,0.000009544517,0.01545967,0.0000409387,0.00003992795,0.00006705815,0.001049121,0.0000387721,0.0003818468],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5330331,"threshold_uncertainty_score":0.7669667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04721143282540416,"score_gpt":0.2684259044813182,"score_spread":0.221214471655914,"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."}}