{"id":"W35926098","doi":"10.1021/acs.molpharmaceut.2c00457","title":"Traffic Signal Timing and Optimization","year":2012,"lang":"en","type":"article","venue":"Transportation research circular","topic":"Traffic control and management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Canadian Institutes of Health Research; Amgen Canada; Terry Fox Foundation","keywords":"Reinforcement learning; Artificial neural network; Control (management); Traffic signal; SIGNAL (programming language); Computer science; Artificial intelligence; Range (aeronautics); Fuzzy logic; Fuzzy control system; Control engineering; Engineering; Machine learning; Real-time computing","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005186887,0.00101688,0.00060143,0.0009529332,0.0006314613,0.001863947,0.001421899,0.0006206341,0.009564285],"category_scores_gemma":[0.001262282,0.0003211446,0.0002657327,0.0008130644,0.0002967165,0.001322783,0.0008908398,0.0007756578,0.00486635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001743351,"about_ca_system_score_gemma":0.001119317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001206132,"about_ca_topic_score_gemma":0.001815512,"domain_scores_codex":[0.9995352,0.00003859862,0.00002549301,0.0001426112,0.0001484139,0.0001096257],"domain_scores_gemma":[0.9994653,0.00008872674,0.00006735473,0.00005546196,0.0002291146,0.00009415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001180965,0.0005765016,0.001956262,0.0004967634,0.00004864373,0.0002121215,0.0001531342,0.06716203,0.6522974,0.03916402,0.01233177,0.2244204],"study_design_scores_gemma":[0.0001008209,0.0006505752,0.001193041,0.00006502345,0.00007242444,0.000295146,0.0001393092,0.4784618,0.4476344,0.0102054,0.06108807,0.00009408365],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2280488,0.003445674,0.6738093,0.001491837,0.001274805,0.0006751899,0.001289042,0.009884105,0.08008118],"genre_scores_gemma":[0.8663004,0.001463572,0.1043644,0.0003538894,0.0001306244,0.0004599976,0.001003749,0.0008946776,0.0250286],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009564285,"threshold_uncertainty_score":0.03199571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03528280440299841,"score_gpt":0.2751892654802742,"score_spread":0.2399064610772758,"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."}}