{"id":"W3161279491","doi":"10.1155/2021/5569143","title":"A Dynamic Bayesian Network-Based Real-Time Crash Prediction Model for Urban Elevated Expressway","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Crash; Dynamic Bayesian network; Bayesian network; Interdependence; Computer science; Traffic flow (computer networking); Artificial intelligence; Data mining; Machine learning; Engineering; Computer security","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":[0.0007047965,0.0008517259,0.000803549,0.0008123403,0.0003933348,0.0006900876,0.001362157,0.0009485558,0.001904778],"category_scores_gemma":[0.00136354,0.0005443572,0.0009007131,0.0005086593,0.0002834209,0.0008274844,0.0005635545,0.001047932,0.000281317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009928372,"about_ca_system_score_gemma":0.001326118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05675747,"about_ca_topic_score_gemma":0.03480902,"domain_scores_codex":[0.9996564,0.00007331589,0.00002218119,0.0001188184,0.00006698241,0.00006234807],"domain_scores_gemma":[0.9996319,0.0001356414,0.00005579506,0.00001029117,0.0001436459,0.00002277871],"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":[0.00003910017,0.00002813899,0.001815891,0.00001477992,0.0000238467,0.000041666,0.00001525214,0.9888598,0.0002827474,0.000530337,0.0002146672,0.008133822],"study_design_scores_gemma":[0.000002317923,0.0000070756,0.0002990737,0.000001333542,0.000005971006,0.000003025188,0.000002239901,0.9994203,0.00003258705,0.0001792226,0.00004445588,0.000002468896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2839329,0.0006073934,0.7080349,0.0005273576,0.0001067519,0.0001061782,0.0009917369,0.0009958401,0.004696967],"genre_scores_gemma":[0.9764253,0.0002368509,0.01962723,0.0000463428,0.00002490372,0.0001681432,0.0006904963,0.00002063928,0.002759934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05675747,"threshold_uncertainty_score":0.1128542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004373617032400156,"score_gpt":0.2063790323495809,"score_spread":0.2020054153171807,"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."}}