{"id":"W4409754032","doi":"10.13052/dgaej2156-3306.4012","title":"An Adaptive Filter Algorithm Based on Hyperbolic Tangent Function for Power Quality Enhancement in Distribution Network","year":2025,"lang":"en","type":"article","venue":"Distributed Generation & Alternative Energy Journal","topic":"Evaluation Methods in Various Fields","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Hyperbolic function; Tangent; Filter (signal processing); Kernel adaptive filter; Distribution (mathematics); Function (biology); Quality (philosophy); Algorithm; Power quality; Mathematics; Adaptive filter; Power (physics); Computer science; Filter design; Mathematical analysis; Physics; Geometry; Computer vision","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004265601,0.0004693956,0.0004216525,0.0003446918,0.0002908225,0.0005019617,0.0005917134,0.0004111144,0.002482508],"category_scores_gemma":[0.000747785,0.0001568417,0.0003537782,0.0006087671,0.0002816879,0.0006040247,0.0002401876,0.0005811609,0.0004971014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005189916,"about_ca_system_score_gemma":0.0007490712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005724512,"about_ca_topic_score_gemma":0.004073081,"domain_scores_codex":[0.999823,0.00003686564,0.0000129424,0.00004213208,0.00007055904,0.0000144361],"domain_scores_gemma":[0.9998016,0.0000667358,0.00002086689,0.00001072033,0.00009363083,0.000006489828],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002001698,0.00009022943,0.0008999123,0.0001585894,0.00005804875,0.00008958218,0.0001389373,0.3452992,0.02178117,0.01275698,0.003201637,0.6153255],"study_design_scores_gemma":[0.0000130544,0.00005566961,0.0002158924,0.000005358692,0.00000793984,0.0000269948,0.000007575923,0.9949589,0.00221817,0.0006828251,0.001801486,0.000006099751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006199874,0.0002077276,0.9919345,0.00005340633,0.00003952994,0.00002797121,0.00001034569,0.0002758613,0.001250808],"genre_scores_gemma":[0.4700881,0.000928955,0.5149365,0.0001180939,0.00008550669,0.0002536804,0.0001851685,0.000116003,0.01328796],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005724512,"threshold_uncertainty_score":0.0113824,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04185014721316515,"score_gpt":0.3468200468950417,"score_spread":0.3049698996818765,"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."}}