{"id":"W2911816503","doi":"10.1155/2019/8591623","title":"Effects of on-Board Unit on Driving Behavior in Connected Vehicle Traffic Flow","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Beijing University of Technology; National Natural Science Foundation of China","keywords":"Microsimulation; Automotive engineering; Vehicle Information and Communication System; Traffic flow (computer networking); Traffic congestion; Traffic conflict; Simulation; Poison control; Computer science; Vehicle-to-vehicle; Transport engineering; Floating car data; Engineering; Road traffic; Computer security; Computer network","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":[0.00006761306,0.00009964525,0.0002242844,0.0002002641,0.000008403136,0.000004192155,0.00006669571,0.00003686923,0.00001336162],"category_scores_gemma":[0.00000829667,0.00009431607,0.00007802435,0.0001472625,0.000005768678,0.0001340534,4.632604e-7,0.0001658747,0.000004177226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003733912,"about_ca_system_score_gemma":0.00000851411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001045631,"about_ca_topic_score_gemma":0.00006544271,"domain_scores_codex":[0.999253,0.00001301053,0.0003559955,0.00007224348,0.0001907178,0.0001149764],"domain_scores_gemma":[0.9996346,0.0001130945,0.0001038783,0.00006984065,0.00004161777,0.00003700751],"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":[0.000153817,0.0001142813,0.0009498678,0.0001212344,0.00002462691,0.00003884308,0.000440383,0.9221593,0.03756081,0.00006220782,0.00000531918,0.03836937],"study_design_scores_gemma":[0.00394386,0.000637807,0.9832457,0.0003580319,0.00006159255,5.533221e-7,0.0001429399,0.005603757,0.00567045,0.00001064162,0.0002103163,0.0001143059],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988371,0.00007269597,0.0002544792,0.00002558547,0.0004648512,0.0002652823,0.000002070837,0.00003316356,0.00004473872],"genre_scores_gemma":[0.9994182,0.00005706013,0.0004483223,0.00001424574,0.00002271723,0.000007252723,0.000004233944,0.00001703715,0.00001091809],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9822959,"threshold_uncertainty_score":0.3846098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003384905132578509,"score_gpt":0.1997149041678911,"score_spread":0.1963299990353126,"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."}}