{"id":"W2356668080","doi":"","title":"URBAN TRANSIT ASSIGNMENT MODEL BASED ON AUGMENTED NETWORK WITH IN-VEHICLE CONGESTION AND TRANSFER CONGESTION","year":2011,"lang":"en","type":"article","venue":"系统科学与系统工程学报(英文版)","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Transit (satellite); Computer science; Urban transit; Traffic congestion; Transfer (computing); Transport engineering; Path (computing); Mathematical optimization; Public transport; Simulation; Computer network; Engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003502604,0.0009539075,0.0008980206,0.0005989607,0.0006134994,0.001278567,0.001715113,0.0008401073,0.004922319],"category_scores_gemma":[0.0006542101,0.0004250357,0.0007828634,0.001054714,0.0006136923,0.002132485,0.001004922,0.0009127759,0.0003900063],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001375302,"about_ca_system_score_gemma":0.0009354139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02235532,"about_ca_topic_score_gemma":0.01555341,"domain_scores_codex":[0.9994789,0.000177314,0.00001722154,0.000140367,0.00008893193,0.00009726261],"domain_scores_gemma":[0.9997234,0.00007382176,0.00006668833,0.00002019122,0.00007944981,0.000036347],"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":[0.00001870864,0.000009803078,0.0001518105,0.00001242138,0.000008617952,0.00006308236,0.00001807831,0.9877108,0.0002157739,0.01007837,0.0003309412,0.001381689],"study_design_scores_gemma":[0.000003966398,0.000009250639,0.00005854396,0.000001398404,0.000005428372,0.00001087445,0.000007568377,0.9968547,0.00003141657,0.002586951,0.0004271753,0.00000278228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1136851,0.000397527,0.8625956,0.0004886913,0.0001333678,0.0001063618,0.0008902255,0.0004376757,0.02126548],"genre_scores_gemma":[0.9558203,0.000418039,0.02640542,0.00004632246,0.00005787498,0.0001820594,0.0004710853,0.00004330735,0.01655563],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02235532,"threshold_uncertainty_score":0.0444504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02722335887137216,"score_gpt":0.232615456467935,"score_spread":0.2053920975965629,"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."}}