{"id":"W4302009578","doi":"10.1155/2022/4092011","title":"Synchronous Optimization for Demand-Driven Train Operation Plan in Rail Transit Network Using Nondominated Sorting Coevolutionary Memetic Algorithm","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Sorting; Computer science; Mathematical optimization; Genetic algorithm; Train; Memetic algorithm; Operations research; Algorithm; Engineering; Mathematics; Machine learning","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":[0.0006694965,0.0006645395,0.0006411505,0.0005177418,0.0004005355,0.0007382632,0.0007836755,0.0008571101,0.001503128],"category_scores_gemma":[0.001267929,0.0003738313,0.0005598777,0.0004990187,0.0004484537,0.0005477776,0.0005941081,0.0005295774,0.00008835927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008641947,"about_ca_system_score_gemma":0.001213845,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01088202,"about_ca_topic_score_gemma":0.007000427,"domain_scores_codex":[0.9997589,0.00007468684,0.00001230176,0.00005378921,0.00005013515,0.00005016231],"domain_scores_gemma":[0.9996383,0.0002134918,0.00004212053,0.00001583432,0.00006357863,0.00002669771],"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":[0.00001491211,0.00001692384,0.0004560222,0.00001503957,0.00001576738,0.00002817764,0.00002254069,0.9898461,0.0003877583,0.001925587,0.0001967417,0.007074437],"study_design_scores_gemma":[0.000004706755,0.000009054856,0.00005075699,0.000001227616,0.000002988672,0.000003293392,0.000004876955,0.9992774,0.00006624305,0.0005080443,0.00007025726,0.000001090419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1503785,0.0003151563,0.8404477,0.0004273572,0.0000507015,0.00009125016,0.00007477847,0.0002009457,0.008013587],"genre_scores_gemma":[0.921496,0.0001462051,0.07476598,0.0001270739,0.00001385998,0.0001808872,0.00008512094,0.00002196412,0.003163064],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01088202,"threshold_uncertainty_score":0.02163738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00794776747389083,"score_gpt":0.212948982914075,"score_spread":0.2050012154401841,"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."}}