{"id":"W2919984182","doi":"10.1155/2019/7481489","title":"Distributed Cooperative Backpressure-Based Traffic Light Control Method","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Project of Shandong Province Higher Educational Science and Technology Program; Natural Science Foundation of Shandong Province","keywords":"Queue; Intersection (aeronautics); Queueing theory; Computer science; Computer network; Bundle; Phase (matter); Distributed computing; Real-time computing; Engineering; Transport engineering","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":[0.0001638255,0.0001413389,0.0003125351,0.0000938572,0.00002068949,0.00001620462,0.00009723467,0.00004705908,0.0000719255],"category_scores_gemma":[0.000006355168,0.000121793,0.0001327328,0.0001451907,0.000006338759,0.0002667087,3.800508e-7,0.0001677732,0.000008999492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004475813,"about_ca_system_score_gemma":0.00002435191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":4.304828e-7,"about_ca_topic_score_gemma":0.0000186197,"domain_scores_codex":[0.9990587,0.00002838024,0.0004457929,0.00009685975,0.0002166553,0.0001535815],"domain_scores_gemma":[0.9994602,0.00007628537,0.000136507,0.00009515719,0.0001610455,0.00007075939],"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":[0.0002252398,0.0000400436,0.00004752862,0.00006193731,0.0001329886,0.00001745359,0.0002370408,0.9736199,0.01308121,0.0001275065,0.0001593936,0.01224972],"study_design_scores_gemma":[0.05653282,0.002154048,0.329765,0.0006949043,0.001534518,0.00001892974,0.001796157,0.3620256,0.01550329,0.0001655615,0.2282305,0.001578651],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4111889,0.0007252579,0.5856304,0.0004866091,0.001103798,0.0004916873,0.00008106074,0.0001314276,0.0001608754],"genre_scores_gemma":[0.9938477,0.00002979047,0.005918347,0.00005668883,0.00005742835,0.000007317934,0.00003631561,0.00001965373,0.00002673545],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6115943,"threshold_uncertainty_score":0.4966575,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003394796444541985,"score_gpt":0.2131083476442932,"score_spread":0.2097135511997512,"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."}}