{"id":"W584275113","doi":"","title":"MEASURING CONGESTION IN THE GREATER TORONTO AREA","year":2004,"lang":"en","type":"article","venue":"Traffic engineering & control","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Metropolitan area; Traffic congestion; Transport engineering; Traffic flow (computer networking); State (computer science); Jurisdiction; Urban area; Computer science; Congestion management; Operations research; Geography; Engineering; Computer security; Economics; Economy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001282095,0.0002180921,0.0001034665,0.001829256,0.0007916062,0.0006825868,0.0002400539,0.0001931588,0.001169552],"category_scores_gemma":[0.0007489688,0.00009709152,0.0001234315,0.003131504,0.0003073185,0.0002918722,0.0003661509,0.000136339,0.0001236182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005116156,"about_ca_system_score_gemma":0.001939815,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8487811,"about_ca_topic_score_gemma":0.9221098,"domain_scores_codex":[0.9997713,0.00002900789,0.00001333327,0.00003959135,0.0001094935,0.00003729201],"domain_scores_gemma":[0.999572,0.00005957887,0.0000984455,0.0000192988,0.0001823926,0.00006831403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002582523,0.00007792711,0.8941745,0.0001836239,0.0001602418,0.000622066,0.005726133,0.02125653,0.009185298,0.002372854,0.009692356,0.05629031],"study_design_scores_gemma":[0.000005032698,0.0000500844,0.9861345,0.00001837601,0.00002143222,0.00008080048,0.0017807,0.007616107,0.0007597862,0.0001343698,0.003380314,0.0000185369],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9917086,0.0002336079,0.0004758017,0.00007591505,0.000005548459,0.00002705714,0.001967119,0.0000339515,0.005472385],"genre_scores_gemma":[0.9970478,0.0002159128,0.0006573355,0.000009302154,0.00000475771,0.00001421315,0.0009255646,0.00000317955,0.001121856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1512189,"threshold_uncertainty_score":0.3042189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01192618114143624,"score_gpt":0.1772042169266314,"score_spread":0.1652780357851952,"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."}}