{"id":"W2893693072","doi":"10.1155/2018/4530787","title":"Optimizing High-Speed Railroad Timetable with Passenger and Station Service Demands: A Case Study in the Wuhan-Guangzhou Corridor","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Railway","keywords":"Column generation; Computation; Scheduling (production processes); Branch and bound; Computer science; Mathematical optimization; Upper and lower bounds; Speedup; Integer programming; Algorithm; 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":[0.0008524647,0.001193564,0.0008011726,0.0006737733,0.0008025213,0.0008878662,0.0009916995,0.001175421,0.002518289],"category_scores_gemma":[0.001069722,0.0004443943,0.0009795765,0.001620197,0.0004324292,0.0009752779,0.0005343815,0.0007648115,0.0001389051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002807565,"about_ca_system_score_gemma":0.003428652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1193991,"about_ca_topic_score_gemma":0.1230133,"domain_scores_codex":[0.9996106,0.0001183603,0.0000146509,0.00007049999,0.00006799999,0.0001178838],"domain_scores_gemma":[0.9994898,0.0002218148,0.00007574895,0.00003531826,0.00009623572,0.00008104108],"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":[0.00004219752,0.00005639667,0.002057355,0.0000462409,0.00002114811,0.000162444,0.00002808646,0.9902855,0.0009145585,0.00116496,0.0003226238,0.004898627],"study_design_scores_gemma":[0.00002672243,0.00009676482,0.001938337,0.00000355995,0.00002380984,0.00003200792,0.0001024793,0.9962753,0.0005793992,0.0004470221,0.0004635741,0.00001110056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8695917,0.0003067075,0.1228022,0.0003273755,0.00003640261,0.0001874351,0.0006052878,0.0002887263,0.005854163],"genre_scores_gemma":[0.9712009,0.000127831,0.02620458,0.00001658316,0.000006946729,0.00008661399,0.0003269012,0.00003433812,0.001995291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1193991,"threshold_uncertainty_score":0.2374083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009008137598488165,"score_gpt":0.2257414583037687,"score_spread":0.2167333207052805,"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."}}