{"id":"W2943827903","doi":"10.1016/j.aap.2019.04.013","title":"Modeling correlation and heterogeneity in crash rates by collision types using full bayesian random parameters multivariate Tobit model","year":2019,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":130,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Tobit model; Crash; Multivariate statistics; Econometrics; Statistics; Bayesian probability; Collision; Mathematics; Computer science; Computer security","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":[0.0003461493,0.0001651802,0.000327711,0.0002672828,0.00005820167,0.00005682408,0.0000729876,0.0001210855,0.00002461079],"category_scores_gemma":[0.00001366158,0.0001648043,0.0001628843,0.0004197754,0.000007161042,0.0003962289,0.00003070063,0.0001157205,0.00000725259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009990046,"about_ca_system_score_gemma":0.000009501552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001596394,"about_ca_topic_score_gemma":0.001408799,"domain_scores_codex":[0.9988911,0.00008262687,0.0004093895,0.0002739243,0.0001591546,0.0001837987],"domain_scores_gemma":[0.9996411,0.00003729739,0.00006912808,0.0001677881,0.00003622795,0.00004850715],"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":[0.00005045194,0.00002090338,0.09504081,0.000004801396,0.0002376498,3.107629e-7,0.0001003138,0.9011368,0.002654823,0.000005486901,0.000004624931,0.0007430927],"study_design_scores_gemma":[0.001066345,0.0000122068,0.01016095,0.00003606292,0.0004861996,5.133546e-7,0.00004694039,0.9874678,0.0004037838,0.0001397494,6.677498e-7,0.0001787889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.558407,0.0002309383,0.441088,0.000003967767,0.00004859453,0.0001666689,5.311659e-7,0.0000395243,0.00001473985],"genre_scores_gemma":[0.9960541,0.0001649611,0.003595442,0.000004824588,0.000008811022,0.000008260205,0.00009994297,0.00001916532,0.00004452143],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4376471,"threshold_uncertainty_score":0.6720524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01248395869974925,"score_gpt":0.2580116591128076,"score_spread":0.2455277004130584,"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."}}