{"id":"W4402306690","doi":"10.1016/j.tra.2024.104239","title":"Segmenting transit ridership: From crisis to opportunity","year":2024,"lang":"en","type":"article","venue":"Transportation Research Part A Policy and Practice","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Market segmentation; Transit (satellite); Transport engineering; Business; Public transport; Engineering; Marketing","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":[0.002179042,0.0001548301,0.0003297708,0.001628702,0.002831595,0.005702413,0.0006553149,0.0007093678,0.002969116],"category_scores_gemma":[0.007247721,0.0002230001,0.000148197,0.002809929,0.003429583,0.005557533,0.003882683,0.00124467,0.0002174051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007675798,"about_ca_system_score_gemma":0.006438112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2157374,"about_ca_topic_score_gemma":0.2825418,"domain_scores_codex":[0.9985741,0.0004187441,0.00008026537,0.0001713163,0.0002712206,0.0004843632],"domain_scores_gemma":[0.9970846,0.0008081504,0.0004995101,0.0001414113,0.0007994202,0.0006669966],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002227335,0.00009726267,0.5064946,0.0003165613,0.00004201006,0.0006121087,0.3508521,0.0005484358,0.0008666483,0.02457946,0.01572992,0.09963829],"study_design_scores_gemma":[0.000005545323,0.00005389717,0.2403516,0.0002719499,0.00001380474,0.0001293165,0.7299121,0.001158032,0.0002122265,0.007020058,0.02083867,0.00003287349],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9601554,0.0009993394,0.00149207,0.01774072,0.00005742728,0.00007205461,0.00053808,0.00002641301,0.0189184],"genre_scores_gemma":[0.9983441,0.0003867918,0.0002650402,0.0002762571,0.00001282046,0.00001494915,0.0001115463,0.000006132277,0.0005824332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2157374,"threshold_uncertainty_score":0.4289634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2459971049122011,"score_gpt":0.5081929464595082,"score_spread":0.262195841547307,"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."}}