{"id":"W2974581475","doi":"10.1139/cjce-2018-0706","title":"Integrated simulation-based dynamic traffic and transit assignment model for large-scale network","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Transit (satellite); Computer science; Schedule; Traffic simulation; Transport engineering; Scale (ratio); Simulation; Public transport; Operations research; Engineering; Microsimulation; Geography","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":[0.0003093869,0.00005949715,0.0001048767,0.0001084205,0.0001090449,0.00003952041,0.00005519256,0.00005625778,0.00004349976],"category_scores_gemma":[0.00003299565,0.00006325427,0.00004079755,0.0001227156,0.000009983115,0.0001077171,2.245973e-7,0.0000820069,3.541583e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009951431,"about_ca_system_score_gemma":0.0005450411,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001255069,"about_ca_topic_score_gemma":0.1968591,"domain_scores_codex":[0.9994721,0.00001115976,0.0001640739,0.00006023889,0.00009288407,0.0001995455],"domain_scores_gemma":[0.9994916,0.0001018554,0.0000541159,0.00003148194,0.00009433861,0.0002265916],"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":[0.00000583088,0.000002197913,0.0007160851,0.00001111837,0.000008152002,0.000001147834,0.003429661,0.995434,0.000006234457,0.0001644779,0.00005731575,0.0001637321],"study_design_scores_gemma":[0.000359152,0.00001998068,0.0006652001,0.00007444252,0.0000161768,2.223981e-7,0.0002384453,0.9951296,5.691478e-7,0.00002104149,0.003402914,0.00007222666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07412426,0.0001345588,0.9250634,0.0001962906,0.0002251886,0.0001212531,0.00002213394,0.00001311902,0.00009980271],"genre_scores_gemma":[0.996056,0.000004353781,0.003730646,0.00004134278,0.00003807729,0.00000150309,0.00001381723,0.00001065983,0.0001036693],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9219317,"threshold_uncertainty_score":0.8177961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008047965901125425,"score_gpt":0.2188533679920104,"score_spread":0.210805402090885,"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."}}