{"id":"W4406142461","doi":"10.1007/s12469-024-00375-6","title":"Modeling passenger loyalty in intercity rail services using a generalized ordered logit model","year":2025,"lang":"en","type":"article","venue":"Public Transport","topic":"Customer Service Quality and Loyalty","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Loyalty; Proxy (statistics); Public transport; Loyalty business model; Transport engineering; Profitability index; Logit; Marketing; Business; Service (business); Customer satisfaction; Econometrics; Logistic regression; Computer science; Service quality; Engineering; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.003748924,0.0009479315,0.001562579,0.001879704,0.001328159,0.004822453,0.002867576,0.003382171,0.008619027],"category_scores_gemma":[0.006795111,0.001419735,0.002244216,0.003027354,0.001313131,0.003370455,0.00227373,0.003239709,0.0007967186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007283421,"about_ca_system_score_gemma":0.004681225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.245028,"about_ca_topic_score_gemma":0.240809,"domain_scores_codex":[0.9968784,0.001438272,0.00009610831,0.0003449161,0.0001731858,0.001069151],"domain_scores_gemma":[0.9942797,0.003986351,0.0006025074,0.0001145046,0.000604336,0.0004125679],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000317304,0.0007948537,0.06231025,0.00006858092,0.0002902643,0.0003184044,0.0004466838,0.9024066,0.0003182535,0.02410732,0.001169049,0.007452362],"study_design_scores_gemma":[0.00003768099,0.0001106275,0.004788416,0.00001187722,0.00008216428,0.00001973747,0.0004724491,0.9912315,0.00007180445,0.00287305,0.0002722622,0.00002841353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.957987,0.0002422407,0.03600169,0.0009900272,0.00005044308,0.0001062852,0.0006677529,0.0001962636,0.003758203],"genre_scores_gemma":[0.9901767,0.0001281732,0.001960598,0.00005504239,0.00001524001,0.00005336673,0.0002580141,0.00001432147,0.007338413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.245028,"threshold_uncertainty_score":0.4872037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0539084763001165,"score_gpt":0.2742706615461192,"score_spread":0.2203621852460027,"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."}}