{"id":"W2018070137","doi":"10.1049/iet-its.2013.0091","title":"Multi‐model direct generalised predictive control for automatic train operation system","year":2014,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"State Key Laboratory of Rail Traffic Control and Safety","keywords":"Model predictive control; Computer science; Control (management); Automatic control; Control engineering; Automotive engineering; Engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000471041,0.0006320649,0.0006078109,0.0003319783,0.0004431242,0.0006755627,0.001188907,0.0005068181,0.001144386],"category_scores_gemma":[0.0005646686,0.0002832991,0.0004352659,0.0004250827,0.0003978354,0.0005142961,0.0006008298,0.000845413,0.0001991313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005435715,"about_ca_system_score_gemma":0.0006398326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008206286,"about_ca_topic_score_gemma":0.007279533,"domain_scores_codex":[0.9997135,0.00004910913,0.00001508297,0.00006129802,0.0001208482,0.00004011574],"domain_scores_gemma":[0.9998208,0.00005590489,0.00002803637,0.00002111277,0.00006486707,0.000009440916],"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":[0.0001191212,0.00005499551,0.0003968807,0.0001662326,0.00005920223,0.000123516,0.00011064,0.8204287,0.01012253,0.008947141,0.001557267,0.1579138],"study_design_scores_gemma":[0.000008078779,0.00003751852,0.00008791778,0.000002943533,0.000005458327,0.00001272829,0.000002876635,0.9980687,0.000663798,0.0005376451,0.0005683042,0.000004005706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01615684,0.0005048351,0.9790403,0.0001199377,0.00009134381,0.00004314677,0.00002102004,0.0004779578,0.003544611],"genre_scores_gemma":[0.9411023,0.0002768275,0.05560542,0.00006739738,0.00004656434,0.00008490813,0.00005048317,0.00002222742,0.002743997],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008206286,"threshold_uncertainty_score":0.01631701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01291879077284484,"score_gpt":0.2160940219567763,"score_spread":0.2031752311839315,"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."}}