{"id":"W326376069","doi":"10.1177/0042098013505883","title":"Traffic Congestion’s Economic Impacts: Evidence from US Metropolitan Regions","year":2013,"lang":"en","type":"article","venue":"Urban Studies","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Metropolitan area; Traffic congestion; Productivity; Gridlock; Per capita; Economics; Panel data; Geography; Economic growth; Transport engineering; Population; Econometrics; Demography; Politics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001464484,0.00008733614,0.0001505583,0.00005518587,0.0004975417,0.00006948759,0.0001077492,0.0000461572,0.000205344],"category_scores_gemma":[0.0003520341,0.00008024171,0.000043933,0.00009471073,0.000260957,0.000433744,0.000006343941,0.00005736941,0.0002344423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004210251,"about_ca_system_score_gemma":0.0001309861,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05047488,"about_ca_topic_score_gemma":0.04892306,"domain_scores_codex":[0.9992666,0.00008141604,0.0001791701,0.0001753115,0.0001055971,0.0001919404],"domain_scores_gemma":[0.9991434,0.0004615636,0.00008626202,0.0001068411,0.0001139383,0.00008798916],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001549343,0.00003810264,0.5202268,0.00001386695,0.0003235032,0.000002935537,0.09884283,0.008779805,0.00002168393,0.01401601,0.3563446,0.001374412],"study_design_scores_gemma":[0.0004013573,0.00008458516,0.8801451,0.0002522016,0.0001647848,3.775422e-7,0.08270284,0.0003814923,0.00001588754,0.002217057,0.03318459,0.0004496869],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9690676,0.0160505,0.0001046075,0.01110876,0.0005247392,0.0002393887,0.00002742144,0.0002316067,0.002645325],"genre_scores_gemma":[0.9947585,0.002436815,0.0006148801,0.0001281067,0.0002294061,0.00003384983,0.00001118744,0.000007023062,0.001780197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3599184,"threshold_uncertainty_score":0.9684316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05979548381541672,"score_gpt":0.3313148149286901,"score_spread":0.2715193311132734,"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."}}