{"id":"W3008084702","doi":"10.1155/2020/3024101","title":"Road Traffic Safety Risk Estimation Method Based on Vehicle Onboard Diagnostic Data","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic and Road Safety","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Chongqing University; Chongqing Research Program of Basic Research and Frontier Technology; National Natural Science Foundation of China","keywords":"Road traffic safety; Cluster analysis; Piecewise; Entropy (arrow of time); Statistics; Computer science; Transport engineering; Road traffic; Engineering; Mathematics","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":[0.0002202752,0.0001588474,0.0002635526,0.00007459716,0.00006747017,0.00001209351,0.0002210086,0.00006925776,0.00002880572],"category_scores_gemma":[0.0001754316,0.0001452884,0.00009373839,0.0002381028,0.00001360287,0.0004936066,0.000001579873,0.000363749,0.00001030011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005026717,"about_ca_system_score_gemma":0.00003728185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001250417,"about_ca_topic_score_gemma":0.00001047339,"domain_scores_codex":[0.998712,0.00005050944,0.0006007509,0.0001682116,0.0003189431,0.0001495979],"domain_scores_gemma":[0.999067,0.0002955619,0.0002255024,0.0001984077,0.00006584524,0.0001476519],"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":[0.000334534,0.00003518439,0.0002055598,0.00006202152,0.00003213825,0.00002677004,0.0005891271,0.8628452,0.0005590031,0.000008046762,0.00006296854,0.1352395],"study_design_scores_gemma":[0.001829348,0.0001845625,0.2787417,0.00008092172,0.0001257156,0.000002097668,0.0001145824,0.717661,0.0005497526,0.00001169942,0.0005566618,0.0001419267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.604381,0.000185399,0.3941399,0.0004532425,0.0003345454,0.0001551896,0.0001834821,0.0001348237,0.00003246588],"genre_scores_gemma":[0.9123598,0.0003020055,0.08692659,0.00006873595,0.0001276894,0.000001791656,0.0001809225,0.00003124259,0.000001269206],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3079787,"threshold_uncertainty_score":0.5924687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01333943970199736,"score_gpt":0.2562189361377371,"score_spread":0.2428794964357398,"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."}}