{"id":"W4399664006","doi":"10.32866/001c.118435","title":"Post-pandemic Recovery of Transit Ridership and Revenue in Canada","year":2024,"lang":"en","type":"article","venue":"Findings","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Revenue; Pandemic; Transit (satellite); Business; Coronavirus disease 2019 (COVID-19); Public transport; Immigration; Population; Demographic economics; Geography; Transport engineering; Economics; Finance; Medicine; Engineering; Environmental health","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003196029,0.00004095478,0.0000908028,0.00004237144,0.00004170113,0.00001819695,0.00007465083,0.00003894305,0.0001265307],"category_scores_gemma":[0.00004103748,0.00004078801,0.00002159284,0.0001881728,0.00005896709,0.0001478761,0.000003351543,0.00009845951,0.000001120399],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000144572,"about_ca_system_score_gemma":0.0009073082,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9467907,"about_ca_topic_score_gemma":0.9946575,"domain_scores_codex":[0.9994711,0.00002352939,0.0001232002,0.0001251367,0.0001238641,0.0001331282],"domain_scores_gemma":[0.9998037,0.00009042605,0.00001315792,0.00004238724,0.00001261902,0.00003773019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001241231,0.0000042234,0.9870132,0.00009318795,0.000005311701,0.00001235163,0.007467595,0.000001001596,0.0003857033,0.0002187608,0.0006952414,0.004091021],"study_design_scores_gemma":[0.0001005122,0.00001612499,0.9871678,0.0001798344,0.00001225953,2.937664e-7,0.001900436,0.000007702148,0.0002780499,0.002235908,0.008000545,0.0001005468],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968293,0.000718281,0.000003892246,0.0007932054,0.0001793714,0.00006750548,0.00007300064,0.00001031141,0.001325138],"genre_scores_gemma":[0.9988765,0.00006355494,0.000009572347,0.00005326081,0.00003175901,0.000001591065,0.000003660056,0.000003493481,0.0009566724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04786672,"threshold_uncertainty_score":0.1663287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01947412265781236,"score_gpt":0.2601502899578498,"score_spread":0.2406761673000375,"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."}}