{"id":"W1845289528","doi":"10.1109/isic.1994.367834","title":"Reducing travel energy costs for a subway train via fuzzy logic controls","year":2002,"lang":"en","type":"article","venue":"","topic":"Railway Systems and Energy Efficiency","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Fuzzy logic; Energy (signal processing); PID controller; Computer science; Energy cost; Control engineering; Engineering; Artificial intelligence; Simulation; Mathematics; Statistics; Architectural engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001552593,0.0001906048,0.0002708544,0.00008405487,0.00007414568,0.00003898484,0.0001359339,0.0001138802,0.0002073753],"category_scores_gemma":[0.00001868285,0.0001629008,0.0001197872,0.000139455,0.00001997661,0.00008988511,0.000005986707,0.00005958766,0.00002725886],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008562557,"about_ca_system_score_gemma":0.000003952318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002369265,"about_ca_topic_score_gemma":0.00008439174,"domain_scores_codex":[0.9989162,0.00001651949,0.0003073976,0.0002170531,0.0001163236,0.0004264935],"domain_scores_gemma":[0.9995548,0.00007435936,0.00002666045,0.000203927,0.0000306844,0.0001095869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002373224,0.0002001465,0.00001527222,0.0001647614,0.0001903652,0.00002466171,0.00116674,0.2199145,0.2433883,0.1460131,0.03957533,0.3493231],"study_design_scores_gemma":[0.001045318,0.000116956,0.00006514834,0.00002930841,0.00001434688,0.00003023471,0.0001315395,0.96395,0.005315556,0.0004692423,0.02844768,0.0003846174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01204775,0.001835575,0.692748,0.0002350667,0.0009146588,0.0003001324,0.00001599647,0.0005795265,0.2913233],"genre_scores_gemma":[0.9903105,0.0000454125,0.001984733,0.0001244342,0.0002560994,0.0001148277,0.000006071868,0.00004408673,0.007113853],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9782627,"threshold_uncertainty_score":0.6642904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01603002940595012,"score_gpt":0.1978962488488797,"score_spread":0.1818662194429295,"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."}}