{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002404765,0.0001829494,0.000157494,0.00008047418,0.0000266082,0.00004533247,0.0001993629,0.00007198469,0.000009448487],"category_scores_gemma":[0.0000152207,0.0001541865,0.00005697787,0.0001054817,0.00001112949,0.0001829031,0.000006448412,0.0001763702,0.00001227347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002390368,"about_ca_system_score_gemma":0.000005739881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002709719,"about_ca_topic_score_gemma":0.0001570307,"domain_scores_codex":[0.9991866,0.00001162672,0.0002064952,0.0001465395,0.0001683739,0.000280393],"domain_scores_gemma":[0.9996838,0.00002759205,0.00001246709,0.000220083,0.00001253525,0.00004350489],"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":[0.000005400735,0.00001851564,0.00002113046,0.00002995307,0.00002866368,0.00001595995,0.0004728194,0.9925005,0.0006946819,0.0009588755,0.0004971944,0.004756263],"study_design_scores_gemma":[0.004713219,0.000117367,0.0398019,0.0002633755,0.00008767015,0.00004767028,0.0002695857,0.9430789,0.000444048,0.00002115688,0.01044613,0.0007089771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5283682,0.001317887,0.4471923,0.0005131615,0.001224412,0.001186379,0.000009335537,0.01549282,0.004695578],"genre_scores_gemma":[0.9992195,0.00006343493,0.0003661088,0.0000822994,0.00008213971,0.0001431219,0.000003460231,0.00003156189,0.000008352536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4708514,"threshold_uncertainty_score":0.6287541,"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."}}