{"id":"W4200522767","doi":"10.1155/2021/5316574","title":"Optimization Problem of Pricing and Seat Allocation Based on Bilevel Multifollower Programming in High-Speed Railway","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Transportation Planning and Optimization","field":"Social Sciences","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Bilevel optimization; Train; Computer science; Mathematical optimization; Revenue; Operations research; Programming paradigm; Optimization problem; Engineering; Economics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001907795,0.001391318,0.002322838,0.0007639058,0.0008923805,0.002364232,0.001461922,0.002124332,0.003159044],"category_scores_gemma":[0.002747446,0.0008981284,0.001719664,0.001839155,0.001014509,0.002244424,0.00167627,0.002255138,0.0002644889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00185058,"about_ca_system_score_gemma":0.002288057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01165288,"about_ca_topic_score_gemma":0.007081704,"domain_scores_codex":[0.9983534,0.0007150341,0.00006158835,0.0002777833,0.0002452659,0.0003469941],"domain_scores_gemma":[0.999055,0.0005899274,0.00009844245,0.0000312821,0.0001298162,0.00009547932],"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":[0.00003879551,0.00004280432,0.0004553978,0.00009350053,0.00003443345,0.000125211,0.00004434387,0.9766353,0.0003823823,0.01558878,0.0005635792,0.005995338],"study_design_scores_gemma":[0.00001021087,0.00001938013,0.00009923974,0.000005121334,0.000009304773,0.00001560249,0.00002501988,0.9932299,0.00009719931,0.006207061,0.0002762491,0.000005716018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0509427,0.0007838138,0.9409728,0.0005201093,0.0000749215,0.00009023013,0.0001647593,0.000117258,0.006333356],"genre_scores_gemma":[0.8905782,0.001052674,0.1013429,0.0001356997,0.00008562939,0.000256817,0.0002723784,0.00008639256,0.006189364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01165288,"threshold_uncertainty_score":0.02317011,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01142471449055754,"score_gpt":0.2697813970449932,"score_spread":0.2583566825544356,"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."}}