{"id":"W4287988197","doi":"10.46298/cst-9845","title":"Development of Indicators to Measure Transit Use Variability Based on Smart Card Data","year":2023,"lang":"en","type":"article","venue":"˜Les œCahiers scientifiques du transport","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Smart card; Ticket; Sample (material); Variance (accounting); Transit (satellite); TRIPS architecture; Service (business); Transport engineering; Computer science; Measure (data warehouse); Public transport; Geography; Statistics; Econometrics; Business; Engineering; Mathematics; Marketing; Database","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":[0.003075447,0.001011212,0.000805697,0.007583587,0.0002604135,0.001774472,0.001008015,0.0006075259,0.001208085],"category_scores_gemma":[0.0116816,0.0003053845,0.000754903,0.006224377,0.0003148928,0.001535557,0.001234877,0.0006967826,0.0007300948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00101978,"about_ca_system_score_gemma":0.001249596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0165222,"about_ca_topic_score_gemma":0.01239337,"domain_scores_codex":[0.9975392,0.0005552329,0.000323866,0.0004463732,0.0009240058,0.0002115132],"domain_scores_gemma":[0.9905598,0.002963689,0.001964729,0.0007593996,0.003375035,0.0003773388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003391902,0.0003713394,0.784435,0.0002554548,0.000267772,0.0001700046,0.0003785288,0.03901143,0.005063671,0.00222624,0.003056413,0.1644248],"study_design_scores_gemma":[0.00004587555,0.0003839659,0.645022,0.0001163717,0.0001287834,0.0001688196,0.0009258905,0.3374134,0.007504654,0.00140835,0.006789643,0.00009228055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6758549,0.0003539095,0.2867188,0.0002006223,0.0001078735,0.001186721,0.02581668,0.002707071,0.00705342],"genre_scores_gemma":[0.8216868,0.0001934959,0.1471844,0.00003411728,0.00004447682,0.0008468741,0.0286131,0.00008558705,0.001311108],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0165222,"threshold_uncertainty_score":0.03285205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05868825634664842,"score_gpt":0.2879614473821595,"score_spread":0.2292731910355111,"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."}}