{"id":"W214799783","doi":"","title":"Getting committed: A new perspective on public transit market segmentation from two Canadian cities","year":2015,"lang":"en","type":"article","venue":"Transportation Research Board 94th Annual MeetingTransportation Research Board","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Market segmentation; Transit (satellite); Public transport; Business; Perspective (graphical); Marketing; Customer base; Cluster analysis; Market research; Transport engineering; Geography; Computer science; Engineering","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","sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.01185051,0.0005921927,0.0007018129,0.00236166,0.003197304,0.0008921011,0.001457839,0.0005303591,0.00273943],"category_scores_gemma":[0.001405155,0.0006483394,0.000340759,0.004010656,0.001870886,0.002370022,0.00001072578,0.002665251,0.000271098],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002600451,"about_ca_system_score_gemma":0.00925714,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9318654,"about_ca_topic_score_gemma":0.9752519,"domain_scores_codex":[0.9826866,0.003420525,0.001370321,0.001722766,0.007650706,0.003149098],"domain_scores_gemma":[0.9854828,0.001913605,0.0002613827,0.0007690956,0.007409277,0.004163885],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002522065,0.000422425,0.6512899,0.00009492759,0.0002760107,0.0003839775,0.249476,0.0005536566,0.0002414943,0.03555783,0.05161996,0.0075617],"study_design_scores_gemma":[0.004163751,0.0005982165,0.5544389,0.000261304,0.00007619758,1.055501e-7,0.3361793,0.0001236336,0.0005227575,0.0223809,0.08028184,0.0009731395],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9109496,0.0005071395,0.0005924572,0.02900191,0.0005033977,0.002936304,0.001245256,0.0004621964,0.05380177],"genre_scores_gemma":[0.989594,0.0001799929,0.001930442,0.0004598308,0.001128452,0.0002768344,0.001199191,0.0001219915,0.005109254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09685101,"threshold_uncertainty_score":0.9996356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1212716047189682,"score_gpt":0.4207730716153991,"score_spread":0.2995014668964309,"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."}}