{"id":"W4390429597","doi":"10.1016/j.aap.2023.107445","title":"How drivers perform under different scenarios: Ability-related driving style extraction for large-scale dataset","year":2023,"lang":"en","type":"article","venue":"Accident Analysis & Prevention","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Key Research and Development Program of China; Science and Technology Commission of Shanghai Municipality; Ministry of Science and Technology of the People's Republic of China","keywords":"Intersection (aeronautics); Driving test; Computer science; Driving simulator; Poison control; Engineering; Simulation; Machine learning; Transport engineering","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":[0.0005137469,0.0002072282,0.0003296526,0.0005198048,0.0002745626,0.00008082512,0.0002182766,0.000248648,0.0002649407],"category_scores_gemma":[0.00003088189,0.0002110584,0.0003751027,0.0009496053,0.00002367955,0.0005549598,0.00008021168,0.0002560228,0.00005628358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000234724,"about_ca_system_score_gemma":0.000009553953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008352255,"about_ca_topic_score_gemma":0.003092212,"domain_scores_codex":[0.9986364,0.00004779629,0.0003671874,0.0003752522,0.0001882572,0.000385145],"domain_scores_gemma":[0.9992239,0.0000829022,0.0001182187,0.0004740889,0.00004029657,0.00006054194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003448298,0.0003615352,0.8053646,0.0000531088,0.006162273,0.000006864775,0.000810131,0.1357936,0.009224074,0.00103334,0.005630487,0.03552548],"study_design_scores_gemma":[0.0005621013,0.00003819032,0.4516798,0.00001915471,0.001531675,0.000001245633,0.001023782,0.540601,0.001223711,0.001742159,0.001301759,0.0002754033],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8982171,0.00005234468,0.09999882,0.0003895664,0.0001664147,0.000347003,0.00001917546,0.0007867448,0.00002280353],"genre_scores_gemma":[0.9934985,0.0001655057,0.0002427298,0.000007672089,0.00003261501,0.00009041413,0.005281791,0.00002756957,0.0006531852],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4048074,"threshold_uncertainty_score":0.8606711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01027382251888873,"score_gpt":0.2587898035496801,"score_spread":0.2485159810307914,"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."}}