{"id":"W2139196926","doi":"10.1016/j.measurement.2008.03.018","title":"Power system frequency estimation using supervised Gauss–Newton algorithm","year":2008,"lang":"en","type":"article","venue":"Measurement","topic":"Power System Optimization and Stability","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Amplitude; Gauss; Algorithm; Convergence (economics); Mathematics; Newton's method; Power (physics); Control theory (sociology); SIGNAL (programming language); Tracking (education); Computer science; Nonlinear system; 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":[0.0004643481,0.0001689888,0.000203201,0.00007752976,0.0001289572,0.00002456674,0.00009947291,0.00007021497,0.0001104654],"category_scores_gemma":[0.00002626568,0.0001681718,0.00006235532,0.0001790683,0.00001832821,0.0001535716,0.00001025253,0.00007801586,0.0000578677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008489921,"about_ca_system_score_gemma":0.00005213031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000412993,"about_ca_topic_score_gemma":0.000004859037,"domain_scores_codex":[0.9985568,0.00006600893,0.0003637632,0.0001833223,0.0005974673,0.0002326616],"domain_scores_gemma":[0.9993559,0.000006487548,0.00003458337,0.0003039817,0.0001999495,0.0000990964],"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":[0.00002004141,0.0003123376,0.008510171,0.001373158,0.0003652264,0.0001044966,0.005504992,0.9343176,0.03994081,0.000783911,0.004268687,0.004498535],"study_design_scores_gemma":[0.0004234575,0.00001991801,0.002108596,0.0001023507,0.00001402411,0.00004210978,0.0001148678,0.9938957,0.002541337,0.000006824196,0.0004920624,0.0002387674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03552214,0.0005115089,0.9540203,0.00002345813,0.001059519,0.0003838894,0.00001073243,0.0006177037,0.007850754],"genre_scores_gemma":[0.9737297,0.000004980922,0.02615852,0.00001047248,0.0000226213,0.00002034779,0.000006795649,0.0000284684,0.00001805755],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9382076,"threshold_uncertainty_score":0.6857846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04213918438552415,"score_gpt":0.2129210679800045,"score_spread":0.1707818835944803,"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."}}