{"id":"W4415151259","doi":"10.1155/atr/2721207","title":"Flexible Train Composition Mode–Based Rolling Stock Circulation Planning Problem for Regional Rapid Rail Transit","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Beijing Municipal Natural Science Foundation","keywords":"Train; Stock (firearms); Adaptability; Urban rail transit; Mode (computer interface); Nonlinear programming; Linear programming; Dynamic programming","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":[0.0009992446,0.001431061,0.001528955,0.0007355819,0.0007419417,0.001852043,0.001260562,0.001369577,0.004858543],"category_scores_gemma":[0.00119724,0.0008652557,0.001402411,0.001017196,0.0005634723,0.001476497,0.001032999,0.001303316,0.0002849931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001843416,"about_ca_system_score_gemma":0.002379648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03062724,"about_ca_topic_score_gemma":0.01811882,"domain_scores_codex":[0.999247,0.0002275838,0.00003795629,0.0001818373,0.0001189352,0.0001867595],"domain_scores_gemma":[0.9995363,0.000203868,0.00006585068,0.00002232403,0.0000831423,0.00008844452],"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":[0.0000641726,0.00002736749,0.0004540862,0.00006594279,0.00002783635,0.0001570643,0.00004749205,0.9892685,0.0006889079,0.003372659,0.000742097,0.005083859],"study_design_scores_gemma":[0.00001063737,0.0000185176,0.0001093386,0.000003715166,0.00001018575,0.00001153763,0.00002875888,0.9983506,0.0001462955,0.001037824,0.0002676235,0.000004940791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2372565,0.001035925,0.7381568,0.001266364,0.0001503564,0.0004324489,0.001174123,0.0006408762,0.01988672],"genre_scores_gemma":[0.943922,0.0003892157,0.04820789,0.00008640792,0.00003006919,0.000216589,0.0006312109,0.00008561464,0.006431018],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03062724,"threshold_uncertainty_score":0.06089795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01530129575104252,"score_gpt":0.2579782412938835,"score_spread":0.242676945542841,"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."}}