{"id":"W2246906915","doi":"10.1109/iisa.2015.7388081","title":"An effective identification of the induction machine parameters using a classic genetic algorithm, NSGA II and θ-NSGA III","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Genetic algorithm; Stator; Induction motor; Robustness (evolution); Identification (biology); Computer science; Automation; Inductance; Engineering; Algorithm; Voltage; Machine learning","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":[0.0003779202,0.0001496645,0.000161707,0.0001273215,0.0002049842,0.00007731641,0.0003801046,0.00006695293,0.000001561263],"category_scores_gemma":[0.00009916149,0.0001138825,0.00003717128,0.0006321263,0.0001488632,0.0008597583,0.0002266666,0.0001232898,0.000001338645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001410062,"about_ca_system_score_gemma":0.00005538475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001429094,"about_ca_topic_score_gemma":0.00001067651,"domain_scores_codex":[0.9985436,0.0002641401,0.0002968835,0.0004376945,0.0002946174,0.0001631064],"domain_scores_gemma":[0.9987005,0.00005687568,0.0002768248,0.0005575442,0.0003006655,0.0001076124],"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":[0.00002302985,0.0003237453,0.001071322,0.0000135888,0.00006222215,0.000002309384,0.004984927,0.2794514,0.02858223,0.001415236,0.00001763453,0.6840523],"study_design_scores_gemma":[0.0006581481,0.0001308078,0.01202173,0.000008805677,0.00001573843,0.00003269282,0.0001407398,0.9625342,0.02262883,0.001681408,0.000009170525,0.0001376561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.144039,0.00005994321,0.8548367,0.00007274517,0.0004062952,0.0004858634,0.00000301861,0.00006896761,0.0000274743],"genre_scores_gemma":[0.5062001,0.000004365462,0.4936544,0.0000400784,0.00002068138,0.0000182474,0.00000165789,0.000009279755,0.00005124023],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6839147,"threshold_uncertainty_score":0.4643992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02222354643715658,"score_gpt":0.280275502639169,"score_spread":0.2580519562020124,"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."}}