{"id":"W4241741489","doi":"10.32920/ryerson.14655642","title":"Adaptive Power Line Harmonic Detection for Active Filter Applications","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Power Quality and Harmonics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Harmonic; Power (physics); Adaptive filter; Line (geometry); Computer science; Electronic engineering; Control theory (sociology); Cover (algebra); Noise (video); Adaptive control; Engineering; Mathematics; Control (management); Acoustics; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001059456,0.0002576485,0.0002854395,0.00008279911,0.00006932016,0.00007883699,0.0001881785,0.0003775053,0.00025542],"category_scores_gemma":[0.00001586375,0.0002853978,0.0002343678,0.00009659639,0.00002235623,0.00009034133,0.0001593396,0.0006101218,0.00003999786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002135274,"about_ca_system_score_gemma":0.00006763818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000191715,"about_ca_topic_score_gemma":0.0001106482,"domain_scores_codex":[0.9989715,0.00002142678,0.0002738385,0.0003726952,0.0001145357,0.000246047],"domain_scores_gemma":[0.9991622,0.00009951808,0.00005579336,0.0004471284,0.0001581471,0.00007722204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006944609,0.001381318,0.000007517825,0.004940424,0.008472663,0.00002324623,0.01630879,0.2902982,0.1051694,0.01558127,0.01569998,0.5414227],"study_design_scores_gemma":[0.001075111,0.0002068765,0.0003134783,0.0002115302,0.0003943184,0.00001046161,0.001962883,0.3060973,0.5531766,0.010689,0.1239387,0.001923728],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004581789,0.0006465201,0.9866117,0.0001129629,0.0007000514,0.001047487,0.0003155404,0.0005159519,0.005467949],"genre_scores_gemma":[0.9858928,0.0002351072,0.01037468,0.000127121,0.0002147516,0.001990071,0.0002943759,0.00007250559,0.0007986423],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.981311,"threshold_uncertainty_score":0.9999598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04760684041986787,"score_gpt":0.2736372086667817,"score_spread":0.2260303682469138,"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."}}