{"id":"W4312729682","doi":"10.1109/pesgm48719.2022.9917225","title":"Induction Machine Emulation for Extreme Weather Conditions","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Power & Energy Society General Meeting (PESGM)","topic":"Real-time simulation and control systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Emulation; Robustness (evolution); Voltage; Computer science; Electric power system; Hardware-in-the-loop simulation; Engineering; Control engineering; Power (physics); Electrical engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001565932,0.0003260626,0.0002239869,0.0001658125,0.0001174901,0.0002351051,0.0002942373,0.0002760434,0.003187711],"category_scores_gemma":[0.0004869848,0.00007285566,0.0001690714,0.0001546595,0.0001348329,0.0003357846,0.0002440508,0.0004494549,0.0004629834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001303843,"about_ca_system_score_gemma":0.0001575603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003956108,"about_ca_topic_score_gemma":0.0002577608,"domain_scores_codex":[0.9999131,0.00002084589,0.000004283525,0.000009066667,0.00004014825,0.00001247521],"domain_scores_gemma":[0.9998558,0.00005885207,0.00002044335,0.00003121153,0.00002772649,0.00000584482],"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":[0.0005646115,0.0001752257,0.003331782,0.0002634017,0.00003905439,0.0004687388,0.000330335,0.7480327,0.1537057,0.008872486,0.002614572,0.08160136],"study_design_scores_gemma":[0.00003392595,0.0002693644,0.00223272,0.00001194746,0.00001282452,0.0001443931,0.00003578536,0.9230063,0.06719991,0.001094518,0.005946426,0.00001178041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3836811,0.0001317652,0.5966403,0.0001672676,0.00008110214,0.0001116094,0.0003507071,0.002434775,0.0164013],"genre_scores_gemma":[0.980699,0.00007098798,0.01621693,0.00002048672,0.000004718968,0.00005039864,0.0001748091,0.0001114515,0.002651212],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003187711,"threshold_uncertainty_score":0.01066399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02376145176440185,"score_gpt":0.2429133267349428,"score_spread":0.219151874970541,"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."}}