{"id":"W4368372082","doi":"10.1177/03611981231166682","title":"Public Transit Itinerary Choice Analysis Considering Various Incentives","year":2023,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Urban Transport and Accessibility","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University; Polytechnique Montréal","funders":"Mitacs","keywords":"Overcrowding; Incentive; Public transport; Mixed logit; Business; Discrete choice; Nested logit; Public economics; Service (business); Microeconomics; Transport engineering; Computer science; Logistic regression; Marketing; Economics; Econometrics; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005645337,0.0009313466,0.001536492,0.001290141,0.0007581851,0.002493902,0.001533045,0.001412784,0.01521922],"category_scores_gemma":[0.01051672,0.0005962092,0.002731365,0.001463642,0.0008229939,0.00225222,0.001125371,0.001842761,0.0005293672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004108444,"about_ca_system_score_gemma":0.002477498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01902652,"about_ca_topic_score_gemma":0.01234275,"domain_scores_codex":[0.9969413,0.001785381,0.00008556011,0.000353786,0.0002261021,0.0006078301],"domain_scores_gemma":[0.9865165,0.01072825,0.0007881273,0.0004455194,0.0007860953,0.0007355759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.005655649,0.00457581,0.08708051,0.0004908041,0.0008415836,0.0009589735,0.001139554,0.7323241,0.003433731,0.11995,0.002449577,0.04109972],"study_design_scores_gemma":[0.00009371428,0.0003646319,0.01027737,0.00001354515,0.0001639999,0.00003854652,0.0004841951,0.9792659,0.0002953257,0.008165553,0.0007977554,0.00003944317],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9317408,0.0001124245,0.06208598,0.0002962936,0.00002689433,0.0003661076,0.000533479,0.00007642496,0.004761499],"genre_scores_gemma":[0.9834278,0.00007155789,0.01091693,0.00002280165,0.00001361133,0.0002534788,0.0003186217,0.00001341102,0.004961847],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01902652,"threshold_uncertainty_score":0.05091333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2052125106359484,"score_gpt":0.4335392070625082,"score_spread":0.2283266964265598,"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."}}