{"id":"W2885927421","doi":"10.1155/2018/6248105","title":"Causation Analysis of Hazardous Material Road Transportation Accidents by Bayesian Network Using Genie","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Bayesian network; Accident (philosophy); Causation; Transport engineering; Hazardous waste; Accident analysis; Flammable liquid; Engineering; Risk analysis (engineering); Computer science; Business; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001488492,0.0001816127,0.0007765973,0.000797303,0.0001685627,0.00006794572,0.0003547757,0.0001206076,0.0004253105],"category_scores_gemma":[0.0001015494,0.0001519685,0.0005767034,0.002983052,0.00009333982,0.001251124,0.000001529174,0.0001190952,0.000003184149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006545297,"about_ca_system_score_gemma":0.00008961176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001575188,"about_ca_topic_score_gemma":0.002017323,"domain_scores_codex":[0.9953537,0.0001549163,0.002353514,0.0002938593,0.001612338,0.0002316767],"domain_scores_gemma":[0.9953833,0.0001341603,0.002634855,0.000272415,0.001447317,0.0001279305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001186962,0.0000993444,0.08110416,0.000007295835,0.001054996,0.00001685465,0.002997046,0.8245599,0.04063476,0.00005448361,0.000179061,0.04810509],"study_design_scores_gemma":[0.0009803795,0.0002930747,0.9714666,0.00004286535,0.003424363,0.000002558646,0.001097693,0.009345581,0.008816945,0.00383856,0.0004646845,0.0002266868],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.803668,0.0001066994,0.1953689,0.00008741,0.0005571434,0.00008355539,0.0001063668,0.000006931862,0.00001499337],"genre_scores_gemma":[0.9865712,0.0001864409,0.01270829,0.00003151427,0.0002707461,0.000001153645,0.0001861352,0.00001417127,0.00003036792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8903624,"threshold_uncertainty_score":0.6197095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02845437452801483,"score_gpt":0.3473402261861374,"score_spread":0.3188858516581226,"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."}}