{"id":"W3186308377","doi":"10.1155/2021/5533722","title":"Simulation Study of Rear-End Crash Evaluation considering Driver Experience Heterogeneity in the Framework of Three-Phase Traffic Theory","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Crash; Microsimulation; Traffic flow (computer networking); Traffic simulation; Computer science; Poison control; Driving simulator; Advanced driver assistance systems; Simulation; Transport engineering; Phase (matter); Engineering; Computer security; 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":[0.0005154655,0.00008550426,0.0002053307,0.00008969518,0.00002056162,0.000007209417,0.00007923926,0.00003332927,0.00002231883],"category_scores_gemma":[0.00007869266,0.00007188255,0.00006746929,0.0002215944,0.000019909,0.0002455614,0.000001366762,0.0001333559,1.005288e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003011529,"about_ca_system_score_gemma":0.00002236738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001414244,"about_ca_topic_score_gemma":0.0003736408,"domain_scores_codex":[0.9987271,0.00009008848,0.0006031079,0.000085919,0.0004125915,0.00008120178],"domain_scores_gemma":[0.9991443,0.0002726654,0.0002244759,0.0001317023,0.0002079366,0.00001889922],"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":[0.0001093309,0.0003262529,0.001207321,0.0000390724,0.00005368889,0.00001970043,0.01780671,0.930511,0.004450116,0.0001020756,2.516176e-7,0.04537449],"study_design_scores_gemma":[0.005213266,0.0004330108,0.873224,0.0002131573,0.000270543,0.000002952732,0.02170345,0.09540197,0.002118564,0.001254326,0.00002548602,0.0001392855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9584247,0.0003524144,0.04072051,0.00001183332,0.0001799935,0.0002906432,0.000002281321,0.000009271314,0.000008342593],"genre_scores_gemma":[0.9986498,0.00003633047,0.001262412,0.000006564383,0.00002171042,0.00001020418,0.000004417463,0.00000822688,3.153977e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8720167,"threshold_uncertainty_score":0.2931286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02379839638613373,"score_gpt":0.3104858351324975,"score_spread":0.2866874387463638,"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."}}