{"id":"W2023757057","doi":"10.1016/j.egypro.2012.09.043","title":"Intelligent Control for Optimal Performance of a Fuel Cell Hybrid Auto Rickshaw","year":2012,"lang":"en","type":"article","venue":"Energy Procedia","topic":"Advanced Battery Technologies Research","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Fuzzy logic; Key (lock); Fuel efficiency; Automotive engineering; Diesel fuel; Controller (irrigation); Minification; Energy management; Computer science; Engineering; Control (management); Work (physics); Energy (signal processing); Control engineering; Artificial intelligence; Mathematics","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":[0.000413687,0.0004183292,0.0002905578,0.0002891518,0.0002654803,0.000729143,0.0003247568,0.0003282937,0.0008622836],"category_scores_gemma":[0.0005830934,0.0001155909,0.0001606092,0.0001420238,0.000266178,0.0002324024,0.0002007737,0.0002632019,0.0001300008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003396015,"about_ca_system_score_gemma":0.0004098038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003451371,"about_ca_topic_score_gemma":0.002928269,"domain_scores_codex":[0.9998863,0.00001616474,0.00000823803,0.00002307475,0.00004438664,0.00002195813],"domain_scores_gemma":[0.9997984,0.0000782375,0.00003864629,0.00001289881,0.00006176106,0.00001021868],"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":[0.0006903287,0.0002624873,0.001477742,0.0002020825,0.00006802813,0.0001738268,0.0002815893,0.7122934,0.1715913,0.006332628,0.000736516,0.10589],"study_design_scores_gemma":[0.00005886993,0.0004785364,0.001215998,0.00001109611,0.00002800123,0.00002800342,0.00005221843,0.9655623,0.03057798,0.0008749505,0.001093326,0.000018741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7159749,0.0003891219,0.2682821,0.0001792744,0.00004492007,0.00009096752,0.00003840429,0.0005391622,0.01446116],"genre_scores_gemma":[0.9950337,0.00003009649,0.004386989,0.00001131001,0.000001857261,0.00001256993,0.000007673972,0.000004613075,0.000511101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003451371,"threshold_uncertainty_score":0.006862581,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01111805253986725,"score_gpt":0.2309835168766559,"score_spread":0.2198654643367887,"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."}}