{"id":"W580991404","doi":"","title":"Measuring Benefits of Adaptive Traffic Signal Control: Case Study of Mill Plain Boulevard, Vancouver, Washington","year":2006,"lang":"en","type":"article","venue":"Transportation Research Board 85th Annual MeetingTransportation Research Board","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Boulevard; Intersection (aeronautics); SIGNAL (programming language); Signal timing; Traffic signal; Adaptive control; Transport engineering; Control (management); Mill; Computer science; Geography; Environmental science; Real-time computing; Engineering; Archaeology; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005346579,0.0004896247,0.0008201476,0.002169403,0.000472531,0.00006466138,0.000618185,0.0003157707,0.00006823807],"category_scores_gemma":[0.00008375842,0.0005314054,0.0002575969,0.002210514,0.0004953762,0.000714245,0.00001298814,0.001420819,0.000007950282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002104875,"about_ca_system_score_gemma":0.0001735781,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01944968,"about_ca_topic_score_gemma":0.1282646,"domain_scores_codex":[0.9909164,0.0008946718,0.001937943,0.0008302136,0.004114555,0.001306171],"domain_scores_gemma":[0.9949559,0.0009028627,0.0002608057,0.0005675142,0.002947367,0.0003655389],"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":[0.002179348,0.002595085,0.02197629,0.001627867,0.0006199966,0.001355304,0.01856627,0.9067445,0.002855563,0.002052704,0.02502957,0.01439745],"study_design_scores_gemma":[0.03430217,0.01475965,0.5357748,0.002564653,0.0009233771,0.00002135868,0.232885,0.1349278,0.03271883,0.0008842306,0.006512712,0.003725469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875422,0.0003531571,0.005242692,0.00004193898,0.0002067597,0.003087624,0.0007977459,0.001704518,0.001023405],"genre_scores_gemma":[0.9976633,0.000164211,0.001170659,0.000006920176,0.0001268145,0.0005046828,0.0001376567,0.0001324796,0.00009322976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7718167,"threshold_uncertainty_score":0.9997138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04609363778497049,"score_gpt":0.2987576315662785,"score_spread":0.252663993781308,"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."}}