{"id":"W4406778085","doi":"10.1049/elp2.12535","title":"High‐dimensional optimal design of dual‐rotor synchronous reluctance machines based on data‐driven torque decomposition","year":2025,"lang":"en","type":"article","venue":"IET Electric Power Applications","topic":"Electric Motor Design and Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dual (grammatical number); Rotor (electric); Magnetic reluctance; Torque; Control theory (sociology); Decomposition; Computer science; Reluctance motor; Control engineering; Engineering; Switched reluctance motor; Mechanical engineering; Physics; Artificial intelligence; Magnet; Chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001813825,0.0002152599,0.0002866281,0.0003863958,0.0001239395,0.0000243615,0.0003989814,0.0001075505,0.00007392745],"category_scores_gemma":[0.00002313332,0.0002229548,0.00007283925,0.001421068,0.0000268158,0.00009110896,0.00002591454,0.0002122689,0.00003046316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001691709,"about_ca_system_score_gemma":0.0001358769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002493288,"about_ca_topic_score_gemma":0.000001974936,"domain_scores_codex":[0.9986586,0.00006213604,0.0003721138,0.0003987262,0.0002284078,0.0002800456],"domain_scores_gemma":[0.9986426,0.0002938558,0.00007921318,0.0008217129,0.00009505515,0.00006751207],"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":[0.00007935359,0.0004008959,0.00007067515,0.00006623496,0.0002814417,0.000004473194,0.00001440219,0.7765942,0.1835064,0.003530892,0.01562199,0.01982905],"study_design_scores_gemma":[0.0003385268,0.00009817328,0.0004333366,0.00003053578,0.0001094344,0.00000292448,4.877107e-7,0.9843979,0.01322459,0.0003237804,0.0008388208,0.0002014562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005067745,0.0006182496,0.9923451,0.0001553646,0.00006104075,0.0007915259,0.00008306761,0.0002543989,0.0006235414],"genre_scores_gemma":[0.9371306,0.00006934057,0.06169525,0.0001491575,0.0000561087,0.0005051424,0.0002627088,0.00003284193,0.00009882652],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9320629,"threshold_uncertainty_score":0.9091831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006675327225430462,"score_gpt":0.2420915575984855,"score_spread":0.235416230373055,"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."}}