{"id":"W2012423843","doi":"10.1109/iecon.2007.4459936","title":"Real-Time Implementation of IPM Motor Protection Using Artificial Neural Network","year":2007,"lang":"en","type":"article","venue":"","topic":"Electric Motor Design and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Digital signal processing; Artificial neural network; Fault (geology); Line (geometry); Computer science; Digital signal processor; Engineering; Control engineering; Electronic engineering; Computer hardware; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0002326049,0.0000699131,0.0001037363,0.00009081226,0.00003712006,0.00001019511,0.00003266533,0.00003754668,0.0002332621],"category_scores_gemma":[0.000002522668,0.000069419,0.00005281067,0.0003619305,0.000005953051,0.00006812005,0.000004019243,0.00004726675,0.000006159098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005251847,"about_ca_system_score_gemma":0.000006663889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000462167,"about_ca_topic_score_gemma":0.00008159627,"domain_scores_codex":[0.9993675,0.00001346488,0.0002452797,0.00007728729,0.0001045714,0.0001919134],"domain_scores_gemma":[0.9998175,0.00001436694,0.00003782929,0.00007189357,0.0000269197,0.0000314985],"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":[0.00001086653,0.000006308294,0.0002904265,0.00001204079,0.00003537198,0.000001142754,0.00003067721,0.02314191,0.9026664,0.0001751544,0.00007887038,0.07355081],"study_design_scores_gemma":[0.00009426465,0.00005704838,0.002868272,0.000004302914,0.00003838621,0.000002142796,0.00002742993,0.8836256,0.1129675,0.0001831653,0.00002636655,0.0001054353],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8128764,0.00001067555,0.1860454,0.000004719971,0.00006574985,0.0001682142,7.819579e-7,0.0001258148,0.0007022464],"genre_scores_gemma":[0.9924238,0.000004562828,0.007206136,0.000004126092,0.0002630657,0.000003450559,0.00000405287,0.00001415429,0.00007662148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8604838,"threshold_uncertainty_score":0.2830825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02039611061088395,"score_gpt":0.2611688533412257,"score_spread":0.2407727427303418,"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."}}