{"id":"W2116193148","doi":"10.1109/tmag.2009.2012694","title":"Multi-Objective Optimization Applied to the Matching of a Specified Torque-Speed Curve for an Internal Permanent Magnet Motor","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Torque; Computer science; Magnet; Control theory (sociology); Direct torque control; Voltage; Induction motor; Physics; Electrical engineering; Engineering","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":[0.001314191,0.0008941835,0.0008533827,0.0006841638,0.0004098897,0.0008162144,0.0006668402,0.001093103,0.001186546],"category_scores_gemma":[0.002196834,0.0004117898,0.0007065571,0.0006993841,0.0005362447,0.0004687083,0.0006610117,0.0008006999,0.0001997783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006617425,"about_ca_system_score_gemma":0.0006189526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001638986,"about_ca_topic_score_gemma":0.00129256,"domain_scores_codex":[0.9995074,0.000210567,0.00002665077,0.00006082807,0.000157631,0.00003680055],"domain_scores_gemma":[0.9993773,0.0003693875,0.00007594598,0.00003513048,0.0001208185,0.00002148934],"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":[0.00001884256,0.00001646057,0.0001131582,0.00003853757,0.0000159562,0.00002613867,0.00002305555,0.9792346,0.001827902,0.002815205,0.0001243381,0.01574586],"study_design_scores_gemma":[0.000002422843,0.00001595054,0.00003873038,0.000003226127,0.000003263938,0.000007321741,0.000002514236,0.9985475,0.0005097025,0.0006303281,0.0002366174,0.000002336436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01041497,0.0001983144,0.9872745,0.00005671581,0.00001939063,0.0000283234,0.000007192296,0.00007951976,0.001921131],"genre_scores_gemma":[0.5535483,0.0003691179,0.4414174,0.00007200157,0.00003471027,0.0002152543,0.00004782974,0.0001034439,0.004191864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001638986,"threshold_uncertainty_score":0.0069502,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02438668565967948,"score_gpt":0.2802916287935153,"score_spread":0.2559049431338358,"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."}}