{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002145939,0.0002536277,0.0002470402,0.0000866198,0.0004374103,0.0001223578,0.0002485621,0.0001463707,0.001011655],"category_scores_gemma":[0.0001946451,0.0002431891,0.000124048,0.000457191,0.00007126241,0.0002998855,0.00004393532,0.0002960984,0.000009437509],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001296481,"about_ca_system_score_gemma":0.00008742031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001293096,"about_ca_topic_score_gemma":0.0002349848,"domain_scores_codex":[0.9968985,0.0009288211,0.0006808839,0.0004997059,0.0005991082,0.0003929956],"domain_scores_gemma":[0.9988471,0.0002055129,0.0003589749,0.0002907388,0.0001693372,0.0001283422],"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":[0.000429436,0.0004987133,0.001805904,0.000001815472,0.00006079592,0.000003359293,0.00007658768,0.8916003,0.004588367,0.01245248,0.01317448,0.07530782],"study_design_scores_gemma":[0.001891918,0.000937697,0.02029024,0.00003662446,0.00004027303,0.000001935965,0.00006774437,0.9293935,0.01728835,0.01054068,0.01918229,0.0003287712],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02190134,0.0000395225,0.97496,0.0005798811,0.001413908,0.0002918731,0.0002545298,0.00002247158,0.0005364522],"genre_scores_gemma":[0.9400844,0.00003154542,0.05397731,0.002277584,0.0006579695,0.0002879723,0.002426397,0.00001961203,0.0002372006],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9209827,"threshold_uncertainty_score":0.9999015,"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."}}