{"id":"W2568444998","doi":"10.1002/atr.1430","title":"Development of efficient stop planning optimization process for high‐speed rail systems","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Heuristics; Process (computing); Decomposition; Computer science; Integer programming; Mathematical optimization; Operations research; Linear programming; Speedup; Network planning and design; Engineering; 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":[0.001524784,0.0008336072,0.000939208,0.0006726832,0.0005471063,0.001188688,0.000871903,0.001077325,0.00285556],"category_scores_gemma":[0.002307314,0.0007245443,0.001105824,0.0007375199,0.0004786649,0.0009914383,0.0008635737,0.001471897,0.0002578972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001313325,"about_ca_system_score_gemma":0.002136811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01195958,"about_ca_topic_score_gemma":0.005980373,"domain_scores_codex":[0.9992494,0.0002687111,0.00003432159,0.0001364153,0.000181556,0.0001296604],"domain_scores_gemma":[0.9988174,0.0006649509,0.000173642,0.00004622324,0.00024268,0.0000551936],"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":[0.00001688302,0.00001819108,0.000242161,0.00002941854,0.000006322726,0.00002363785,0.00002818156,0.9900998,0.0005137794,0.00338787,0.0002021865,0.00543155],"study_design_scores_gemma":[0.000002719343,0.000008833294,0.00003811384,0.000002424349,0.000001918529,0.000002306916,0.000006532845,0.9988966,0.0001940811,0.0007199561,0.0001250179,0.00000142198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03659648,0.0001198732,0.9590852,0.0001430229,0.00001574284,0.00009121661,0.00007912576,0.0001631831,0.00370613],"genre_scores_gemma":[0.7305702,0.0002227205,0.266033,0.00005185969,0.00001701399,0.0002776441,0.0002559891,0.00006833908,0.002503361],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01195958,"threshold_uncertainty_score":0.02377993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01135467250907848,"score_gpt":0.2355590435183579,"score_spread":0.2242043710092794,"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."}}