{"id":"W1815011342","doi":"10.1139/cjce-2013-0427","title":"Assessing the mobility benefits of proactive optimal variable speed limit control during recurrent and non-recurrent congestion","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Traffic control and management","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"VisSim; Speed limit; Bottleneck; Traffic congestion; Queue; Duration (music); Traffic flow (computer networking); Control (management); Network congestion; Computer science; Simulation; Flow control (data); Variable (mathematics); Transport engineering; Microsimulation; Engineering; Computer network; Mathematics; Network packet","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004810153,0.0001677193,0.0002892146,0.0001958493,0.00004727531,0.00009105523,0.0001467813,0.00005308463,0.00001195463],"category_scores_gemma":[0.0001372991,0.0001431526,0.0000568231,0.0001380704,0.00003070542,0.0003237606,0.00001263972,0.0002938927,3.962314e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002624297,"about_ca_system_score_gemma":0.0001887972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001479988,"about_ca_topic_score_gemma":0.003008326,"domain_scores_codex":[0.9990375,0.00001565703,0.0003737679,0.0001028261,0.0001710892,0.0002991443],"domain_scores_gemma":[0.9991157,0.00006971961,0.0001022833,0.0001281599,0.0001722408,0.0004119128],"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":[0.00001546382,0.00001170627,0.0002428576,0.0001099647,0.0001627065,0.000008900737,0.0004270333,0.9931644,0.0005092064,0.000117106,0.00006051089,0.0051701],"study_design_scores_gemma":[0.003829314,0.0002608922,0.1668541,0.0008732447,0.0003691023,0.0001374944,0.0008037352,0.8233721,0.0004881233,0.00003440909,0.002472029,0.0005054337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9804478,0.004694241,0.01264314,0.0001040233,0.001124876,0.00025221,0.00001750916,0.00003262775,0.000683599],"genre_scores_gemma":[0.999456,0.00004581047,0.0002965938,0.000003827245,0.0001642472,0.000005473333,0.000001036737,0.00002183945,0.000005158911],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1697923,"threshold_uncertainty_score":0.5837594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01360421072274411,"score_gpt":0.2007661815979036,"score_spread":0.1871619708751595,"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."}}