{"id":"W4252234954","doi":"10.32920/ryerson.14654922.v1","title":"Exploring Transit Performance And Traffic Congestion in Downtown Toronto Using Big Data","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of British Columbia","funders":"","keywords":"Downtown; Headway; Transport engineering; Traffic congestion; Transit (satellite); Reliability (semiconductor); Computer science; Twin cities; Geography; Public transport; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0002905988,0.0005280391,0.0001672627,0.0009272873,0.0004698999,0.001092303,0.0003360478,0.0002137601,0.0009284167],"category_scores_gemma":[0.00212851,0.0002030531,0.0002237009,0.002776503,0.0004562289,0.000571861,0.0006412679,0.0003963347,0.0001289552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005356987,"about_ca_system_score_gemma":0.003134651,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8755372,"about_ca_topic_score_gemma":0.9302553,"domain_scores_codex":[0.9997681,0.00004636538,0.00001209271,0.00005129581,0.00006988196,0.0000523567],"domain_scores_gemma":[0.9989671,0.0003124429,0.0001845023,0.00009910831,0.0002831293,0.0001537408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001730921,0.00006685112,0.9016013,0.0002193114,0.0002257908,0.0006767802,0.003264608,0.0575419,0.002653221,0.003252826,0.007534442,0.02278999],"study_design_scores_gemma":[0.000008171001,0.00003632618,0.9269125,0.00006354962,0.00007112999,0.00005994609,0.006161727,0.05698758,0.0009562913,0.0007020824,0.008001762,0.00003902295],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9864733,0.0003099861,0.001731117,0.000477348,0.00001329073,0.0000202237,0.008866252,0.00006474381,0.002043679],"genre_scores_gemma":[0.9911845,0.0002535141,0.001184048,0.00002550519,0.000007695568,0.000009681048,0.006613689,0.00001068309,0.0007106312],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1244628,"threshold_uncertainty_score":0.2503916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2080661388990411,"score_gpt":0.2597099297401178,"score_spread":0.05164379084107673,"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."}}