{"id":"W2756577398","doi":"10.1155/2017/3082781","title":"Will Automated Vehicles Negatively Impact Traffic Flow?","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":197,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bottleneck; Traffic flow (computer networking); Cripple; Truck; Transport engineering; Computer science; Traffic simulation; Automation; Traffic wave; Simulation; Engineering; Traffic congestion reconstruction with Kerner's three-phase theory; Traffic congestion; Automotive engineering; Microsimulation; Computer security","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":[0.00009166695,0.0001192838,0.0002017336,0.00008424948,0.00009909426,0.00004685178,0.0001591639,0.00003556751,0.00001568884],"category_scores_gemma":[0.0000125003,0.00009989851,0.0001396501,0.00004099243,0.00002167472,0.001149194,9.441858e-7,0.0001229951,0.000002827187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005090093,"about_ca_system_score_gemma":0.00001748163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002278581,"about_ca_topic_score_gemma":0.00004883653,"domain_scores_codex":[0.9992743,0.000006585793,0.0003384069,0.00006594471,0.0001716938,0.0001430749],"domain_scores_gemma":[0.9994774,0.00001784639,0.0002190908,0.0001257045,0.00008599505,0.00007388886],"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":[0.00006740943,0.00002020816,0.00008261678,0.0000258547,0.00009727376,0.00003710462,0.0005907241,0.8965949,0.003802533,0.00002429321,0.0001744028,0.09848267],"study_design_scores_gemma":[0.00227127,0.0001644487,0.9272225,0.00009752509,0.00009068629,0.000006168224,0.0001609718,0.06733624,0.0002750967,0.0001117357,0.002101273,0.0001620776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957029,0.0002481067,0.002965791,0.0001344988,0.0005150802,0.00009796449,0.0000140678,0.0002124793,0.0001091659],"genre_scores_gemma":[0.9963868,0.0001844795,0.003283739,0.000007525995,0.00009473499,0.000002344157,0.00000672897,0.00001870701,0.00001488947],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9271399,"threshold_uncertainty_score":0.4073743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006438747598037913,"score_gpt":0.2430655203890703,"score_spread":0.2366267727910324,"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."}}