{"id":"W4399734102","doi":"10.1109/tie.2024.3398669","title":"Intelligent Efficient Control for Brushless Doubly-Fed Induction Machines","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Electronics","topic":"Multilevel Inverters and Converters","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; École de Technologie Supérieure; University of Saskatchewan","funders":"","keywords":"Machine control; Induction motor; Control engineering; Computer science; Control theory (sociology); DC motor; Direct torque control; Control (management); Engineering; Electrical engineering; Artificial intelligence; Voltage","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001827039,0.0002662748,0.0002374463,0.0002395957,0.0001384677,0.0001196025,0.0001449644,0.0002826958,0.0001046558],"category_scores_gemma":[0.000003218153,0.0002557022,0.0002232888,0.0002953968,0.00003206933,0.00009234649,4.441477e-7,0.0007178786,0.0000470609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004535505,"about_ca_system_score_gemma":0.0001276848,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003092933,"about_ca_topic_score_gemma":0.00003313809,"domain_scores_codex":[0.9986959,0.00003001497,0.0003318607,0.000298444,0.0001844851,0.0004593655],"domain_scores_gemma":[0.9995202,0.0001259187,0.00002108415,0.0001930811,0.00003871117,0.0001009333],"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":[0.0002494924,0.0000508193,5.202256e-7,0.00004726973,0.0002939273,0.000002051066,0.0001423981,0.3901083,0.003999305,0.0004093449,0.001552738,0.6031438],"study_design_scores_gemma":[0.00122189,0.0002265961,6.357772e-7,0.00006235918,0.0001316264,0.000009137063,0.00002668428,0.9306242,0.05316124,0.0001022558,0.01415887,0.0002744834],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02262056,0.0004199958,0.9681377,0.0002329895,0.007025982,0.0007455832,0.0001113426,0.0005881981,0.0001176048],"genre_scores_gemma":[0.9988182,0.000107664,0.00005179215,0.00006950489,0.0004005073,0.0002571435,0.000009896333,0.0000713962,0.0002138715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9761977,"threshold_uncertainty_score":0.9999895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03011791884823484,"score_gpt":0.2489227818443759,"score_spread":0.218804862996141,"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."}}