{"id":"W4413498809","doi":"10.1016/j.aap.2025.108211","title":"Safety evaluation for heavy vehicle drivers using extreme value model based on the multi-source sensing data","year":2025,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Traffic and Road Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Basic Research Program of Jiangsu Province; National Key Research and Development Program of China; China Scholarship Council; National Natural Science Foundation of China","keywords":"Poison control; Engineering; Transport engineering; Value (mathematics); Automotive engineering; Occupational safety and health; Computer science; Simulation; Reliability engineering; Forensic engineering; Medical emergency; Medicine","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":[0.001536645,0.0001662819,0.0002234802,0.0002777857,0.0003151775,0.0000633234,0.000288811,0.00008517903,0.00003175851],"category_scores_gemma":[0.00009938426,0.000143489,0.0002755945,0.0007435665,0.00001724743,0.0002313429,0.00006769947,0.0001191797,0.00000387065],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002777185,"about_ca_system_score_gemma":0.00006579644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004724165,"about_ca_topic_score_gemma":0.0007045645,"domain_scores_codex":[0.9985583,0.0001688445,0.0003771405,0.0003542856,0.0003344043,0.0002070312],"domain_scores_gemma":[0.9988327,0.0001660612,0.00008984673,0.0007787169,0.0001004553,0.00003223096],"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":[0.00003059938,0.00003324233,0.002602967,0.000004982327,0.0005365168,1.268162e-7,0.00006531429,0.9692858,0.0005010764,0.00008434731,0.0001897475,0.02666521],"study_design_scores_gemma":[0.0006825197,0.000006260513,0.00848109,0.00007019065,0.003124839,9.667535e-8,0.0001071214,0.9868038,0.0003028974,0.000155006,0.0001258908,0.0001402795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1384025,0.00009480522,0.8604447,0.0002100014,0.0001089334,0.0005078036,0.000003104191,0.0001145118,0.0001136436],"genre_scores_gemma":[0.9823698,0.00001782872,0.01688246,0.00007437732,0.00003489585,0.000008760652,0.0003940893,0.00001949693,0.0001982651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8439673,"threshold_uncertainty_score":0.5851312,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08852415963737523,"score_gpt":0.3253838929837645,"score_spread":0.2368597333463893,"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."}}