{"id":"W1938281091","doi":"10.1109/pes.2004.1373081","title":"Synchronous generator model identification using Volterra series","year":2004,"lang":"en","type":"article","venue":"IEEE Power Engineering Society General Meeting, 2004.","topic":"Control Systems and Identification","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Volterra series; Generator (circuit theory); Control theory (sociology); Convolution (computer science); Generalization; Nonlinear system; Computer science; Permanent magnet synchronous generator; Voltage; Series (stratigraphy); Volterra integral equation; Applied mathematics; Power (physics); Mathematics; Integral equation; Engineering; Mathematical analysis; Artificial intelligence; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003501915,0.0003857263,0.0003182816,0.00008546322,0.0001762069,0.0002143192,0.0002424696,0.0002166747,0.00001029672],"category_scores_gemma":[0.000023295,0.000449531,0.0002765327,0.000279007,0.00003060073,0.0004813824,0.00002217661,0.0002367644,0.00004032753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006484623,"about_ca_system_score_gemma":0.00006684307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001008043,"about_ca_topic_score_gemma":0.00001295398,"domain_scores_codex":[0.9981027,0.00001421542,0.0006065119,0.0004061891,0.0003192984,0.0005511257],"domain_scores_gemma":[0.9991449,0.000009199728,0.000105,0.0004658349,0.0001255348,0.0001495251],"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":[0.00000104746,0.00001082048,0.000008688491,0.00005498205,0.00005298936,9.658747e-7,0.0004892896,0.5597434,0.4384497,0.0001454979,0.0009942654,0.00004838542],"study_design_scores_gemma":[0.0003999043,0.00001287627,0.00009480058,0.00008340779,0.00004198942,0.00002184853,0.0000478243,0.920134,0.07756991,0.0000437348,0.001057786,0.0004919162],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5358112,0.001472166,0.4596119,0.00003716258,0.0020268,0.0002372212,0.00002073283,0.0006965701,0.00008623954],"genre_scores_gemma":[0.9773944,0.00007605649,0.02116969,0.00003615543,0.0007180099,0.00006749993,0.00002214074,0.0001447072,0.0003713339],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4415832,"threshold_uncertainty_score":0.9997956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007923703237206385,"score_gpt":0.2042010222895251,"score_spread":0.1962773190523187,"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."}}