{"id":"W3093588346","doi":"10.1155/2020/8828939","title":"Predicting Wet-Road Crashes Using the Finite-Mixture Zero-Truncated Negative Binomial Model","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Changsha University of Science and Technology; National Natural Science Foundation of China; Education Department of Hunan Province","keywords":"Crash; Negative binomial distribution; Markov chain Monte Carlo; Sample (material); Monte Carlo method; Transport engineering; Statistics; Computer science; Engineering; Mathematics; Poisson distribution","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003400388,0.000757026,0.001399446,0.001512951,0.0005468386,0.001309135,0.003338749,0.001183704,0.002688674],"category_scores_gemma":[0.007851155,0.0007027619,0.00167464,0.0009469502,0.0007272692,0.001731483,0.001023053,0.001447857,0.000569219],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001119621,"about_ca_system_score_gemma":0.0009705232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03195982,"about_ca_topic_score_gemma":0.01936836,"domain_scores_codex":[0.9987179,0.0005597927,0.00006050906,0.0003175205,0.0001799862,0.0001642883],"domain_scores_gemma":[0.9957312,0.002932963,0.0004562353,0.0001900245,0.0005492269,0.000140371],"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":[0.0002522735,0.0001911589,0.0368825,0.00009103028,0.0001334158,0.0002643861,0.0001747477,0.9281881,0.001008124,0.0096909,0.001092872,0.02203037],"study_design_scores_gemma":[0.000009461945,0.00002871405,0.002622638,0.000007286351,0.00002086222,0.0000262006,0.00003146081,0.9949585,0.00009858616,0.002052665,0.0001321587,0.00001155535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5468809,0.0007334098,0.4475858,0.0005336286,0.0001068415,0.0001750733,0.001209384,0.0005038474,0.002271168],"genre_scores_gemma":[0.9734725,0.0003804017,0.02224334,0.00007363398,0.00003928782,0.0001576318,0.001490592,0.00003287934,0.002109709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03195982,"threshold_uncertainty_score":0.06354761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01488175584703625,"score_gpt":0.2256855263729553,"score_spread":0.2108037705259191,"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."}}