{"id":"W4312133923","doi":"10.1155/2022/7035214","title":"Headway Optimisation for Metro Lines Based on Timetable Simulation and Simulated Annealing","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Anhui Provincial Key Research and Development Plan","keywords":"Headway; Simulated annealing; Train; Simulation; Block (permutation group theory); Randomness; Monte Carlo method; Interval (graph theory); Line (geometry); Blocking (statistics); Computer science; Engineering; Automotive engineering; Algorithm; Mathematics; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002535715,0.000088646,0.000162481,0.0001833972,0.0001137429,0.0000126833,0.00004027525,0.00002614077,0.00001312724],"category_scores_gemma":[0.00001966506,0.00008744161,0.00006387118,0.0001840989,0.000005022531,0.0002145513,4.887913e-7,0.00009396861,1.172562e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005846141,"about_ca_system_score_gemma":0.00001461894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003412627,"about_ca_topic_score_gemma":0.000003999119,"domain_scores_codex":[0.9992133,0.00001629924,0.0003896188,0.0000795454,0.0001992844,0.0001019207],"domain_scores_gemma":[0.9995093,0.0001360993,0.0001539356,0.00005292292,0.000109056,0.00003873057],"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":[0.0001472513,0.00002026751,0.0001493699,0.00004408394,0.00001437815,0.000001684693,0.0002762565,0.9907324,0.005070876,0.0000275748,0.00000711643,0.003508722],"study_design_scores_gemma":[0.001106081,0.0003294342,0.003213963,0.00002750847,0.00003190537,7.588235e-7,0.0002129394,0.9915266,0.00147052,0.00006135536,0.001926112,0.00009283624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8206593,0.0002701823,0.1784161,0.00003405023,0.0004052769,0.0001401563,0.00001804077,0.0000352628,0.00002164766],"genre_scores_gemma":[0.9918589,0.000008940757,0.007942026,0.00002232907,0.00007220004,0.000007131792,0.00005197249,0.00002160872,0.00001490594],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1711996,"threshold_uncertainty_score":0.3565766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01184119396840026,"score_gpt":0.2466906744767483,"score_spread":0.234849480508348,"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."}}