{"id":"W2103190026","doi":"10.1109/iacet.1995.527651","title":"Accelerating performance evaluation of deep-bar induction machines from parameter identification","year":2002,"lang":"en","type":"article","venue":"","topic":"Electric Motor Design and Analysis","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Leakage inductance; Inductance; Bar (unit); Control theory (sociology); Induction motor; Rotor (electric); Torque; Acceleration; Leakage (economics); Computer science; Machine control; Voltage; Engineering; Control engineering; Artificial intelligence; Mechanical engineering; Electrical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007140567,0.0006057506,0.000367002,0.0007792899,0.0001370839,0.0003147436,0.0002431522,0.0002914493,0.0007293053],"category_scores_gemma":[0.001898173,0.00013631,0.0001783109,0.000376968,0.0001743838,0.0004110131,0.0003008024,0.0002762566,0.0003811558],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001855802,"about_ca_system_score_gemma":0.0001615215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003928821,"about_ca_topic_score_gemma":0.0002758211,"domain_scores_codex":[0.9996784,0.00006665551,0.00001810393,0.00002820235,0.0001841248,0.00002459729],"domain_scores_gemma":[0.9991179,0.0003542401,0.0001249753,0.0001374569,0.0002304123,0.00003495466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001205176,0.0001901827,0.01114364,0.0003792504,0.0000419124,0.0002183034,0.000284111,0.2864855,0.4087515,0.002809877,0.0006221393,0.2878684],"study_design_scores_gemma":[0.00004774808,0.00193881,0.02862129,0.0000343492,0.00005824459,0.0002951255,0.00007952593,0.6696513,0.2946429,0.001654258,0.002918431,0.00005812441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8437392,0.0004707624,0.1477198,0.0000322214,0.00001166294,0.00005368146,0.0001278688,0.001223775,0.006621112],"genre_scores_gemma":[0.9881754,0.0001007754,0.01079679,0.000003027307,0.000003668079,0.00001230662,0.0001866315,0.00004407061,0.0006773753],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007792899,"threshold_uncertainty_score":0.003776371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04683186284244972,"score_gpt":0.245343352549806,"score_spread":0.1985114897073562,"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."}}