{"id":"W2275879607","doi":"","title":"Big Data in Transportation Program Management: Findings and Interpretations from the City of Toronto","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Big data; Traffic congestion; Computer science; Analytics; Data science; Baseline (sea); Transport engineering; Business; Engineering; Political science; Data mining","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003363153,0.0003177357,0.0003771304,0.0005110195,0.0002791758,0.00009619406,0.001075895,0.000210703,0.0001164928],"category_scores_gemma":[0.0001207531,0.0002469892,0.00009049027,0.001072838,0.0006990617,0.001225469,0.00002483654,0.0006505486,0.00001020362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001761222,"about_ca_system_score_gemma":0.00007743599,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.016706,"about_ca_topic_score_gemma":0.1762783,"domain_scores_codex":[0.9947653,0.0003927936,0.001135487,0.0008467723,0.001968995,0.0008906683],"domain_scores_gemma":[0.9970751,0.0010192,0.00009062566,0.000924403,0.0006375572,0.0002530981],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001215392,0.0008029085,0.3510997,0.001420704,0.0006351485,0.000133877,0.0293808,0.0003602743,0.006550021,0.01768307,0.02380424,0.5669138],"study_design_scores_gemma":[0.001510292,0.0002137876,0.968658,0.0006747541,0.00005549565,9.65109e-8,0.005951158,0.001033476,0.0008882844,0.0007912129,0.01991777,0.0003056224],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9759344,0.0007072347,0.01296348,0.001229225,0.0002507399,0.003055971,0.00261552,0.001333923,0.001909466],"genre_scores_gemma":[0.989477,0.005263751,0.003323446,0.00002835522,0.00008473667,0.0007399468,0.0009056038,0.00006741106,0.0001096994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6175584,"threshold_uncertainty_score":0.9999982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06312102226188653,"score_gpt":0.3548864655462998,"score_spread":0.2917654432844132,"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."}}