{"id":"W1868493963","doi":"10.1109/pess.2002.1043559","title":"A new contribution into performance of active power filter utilizing SVM based HCC technique","year":2003,"lang":"en","type":"article","venue":"","topic":"Power Quality and Harmonics","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Support vector machine; Computer science; Power (physics); Active filter; Artificial intelligence; Engineering; Electrical engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002440426,0.000333253,0.0003584594,0.0003500977,0.0001802214,0.0005642919,0.0004838419,0.0004228852,0.00264295],"category_scores_gemma":[0.0007474516,0.00008923201,0.0001645106,0.0003502023,0.0001620599,0.000880915,0.0001623178,0.000449375,0.0005959601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001934028,"about_ca_system_score_gemma":0.0001784661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003963348,"about_ca_topic_score_gemma":0.0003361662,"domain_scores_codex":[0.9997278,0.0000363953,0.00001720764,0.00004097376,0.0001585495,0.00001912473],"domain_scores_gemma":[0.9994487,0.0002039034,0.00002689118,0.00006011255,0.0002405988,0.00001976989],"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":[0.0002285501,0.0001283735,0.001165219,0.0002487137,0.0000323142,0.0001031745,0.000109659,0.01571809,0.1385287,0.01144572,0.001069108,0.8312224],"study_design_scores_gemma":[0.00004530301,0.001031516,0.002321826,0.00003775243,0.00006874066,0.0008133443,0.00005421833,0.7557595,0.207424,0.005711018,0.02670142,0.00003133239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0355195,0.001197957,0.9549484,0.0001277015,0.000111837,0.0000268418,0.00001634968,0.0009954026,0.00705587],"genre_scores_gemma":[0.807308,0.001183131,0.1848397,0.00008638463,0.0002818559,0.00003220544,0.00009645563,0.0001187285,0.006053588],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00264295,"threshold_uncertainty_score":0.008841574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01368716357130305,"score_gpt":0.2353276104012029,"score_spread":0.2216404468298999,"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."}}