{"id":"W2899057210","doi":"10.1155/2018/1784789","title":"A Sparse Optimization Approach for Energy-Efficient Timetabling in Metro Railway Systems","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"State Key Laboratory of Rail Traffic Control and Safety; Beijing Jiaotong University; National Natural Science Foundation of China","keywords":"Mathematical optimization; Train; Computer science; Relaxation (psychology); Optimization problem; Energy (signal processing); Convex optimization; Efficient energy use; Algorithm; Regular polygon; Mathematics; Engineering","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":[0.0003670428,0.0001335838,0.0002861501,0.0003471449,0.00004415552,0.00002502142,0.00009855018,0.00007077255,0.000004443723],"category_scores_gemma":[0.00001576645,0.0001211519,0.00009310248,0.0004159095,0.00001804465,0.000250608,6.773024e-7,0.0000798244,3.005701e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008593477,"about_ca_system_score_gemma":0.00002390087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000135951,"about_ca_topic_score_gemma":0.0000240014,"domain_scores_codex":[0.9987291,0.00001841844,0.0007114004,0.0001198939,0.0002196006,0.0002015583],"domain_scores_gemma":[0.9993559,0.0000335676,0.0002170144,0.00009075447,0.000240811,0.000061939],"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":[0.00005959067,0.00005126588,0.00006213433,0.0000748606,0.0000232843,0.00000250394,0.0004914494,0.9943088,0.00273668,0.0004389761,0.00001348048,0.00173698],"study_design_scores_gemma":[0.001059145,0.0001382889,0.0006784445,0.0000953372,0.00002960106,0.00000544493,0.000760529,0.9953369,0.001173588,0.000009791307,0.0005747553,0.0001381654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1931837,0.0007658254,0.8048119,0.0000035607,0.0008215517,0.0001233716,0.000005593822,0.0000295841,0.0002549064],"genre_scores_gemma":[0.9084833,0.00005266439,0.0910915,0.000004290868,0.0002731015,0.00002116497,0.00002369795,0.00002940867,0.00002083273],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7152996,"threshold_uncertainty_score":0.494043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00989139750317891,"score_gpt":0.2131367288953372,"score_spread":0.2032453313921583,"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."}}