{"id":"W156861896","doi":"","title":"INNOVATIVE POWER SYSTEM TRANSIENT DISTURBANCES DETECTION AND CLASSIFICATION USING WAVELET ANALYSIS","year":2004,"lang":"en","type":"article","venue":"","topic":"Power Quality and Harmonics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Wavelet; Multiresolution analysis; Power quality; Computer science; Transient (computer programming); Electric power system; Wavelet transform; Pattern recognition (psychology); Artificial intelligence; Power (physics); Electronic engineering; Engineering; Discrete wavelet transform; Voltage; Electrical engineering","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":[0.0001128011,0.00007796354,0.0001223199,0.0001261877,0.00005595497,0.00003121719,0.00002942235,0.00004815414,0.00000537493],"category_scores_gemma":[0.000003258296,0.00007182432,0.00003010608,0.000790465,0.000021127,0.0001240552,0.000003555689,0.00006726971,0.000003092116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001603946,"about_ca_system_score_gemma":0.000007185993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003868632,"about_ca_topic_score_gemma":0.00008169463,"domain_scores_codex":[0.9995154,0.00001180326,0.0001679927,0.0001141027,0.00008581816,0.0001049505],"domain_scores_gemma":[0.9998087,0.000009077191,0.00002297572,0.00008828355,0.00004345,0.00002752432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009245292,0.0001991345,0.002327217,0.001018726,0.004311244,0.00001785243,0.02342007,0.2860016,0.5190651,0.1446505,0.00005248819,0.01884364],"study_design_scores_gemma":[0.0009825977,0.00007463813,0.1674691,0.00008083711,0.0005534971,0.00001733138,0.006338393,0.7597425,0.0621345,0.0002830059,0.001630631,0.0006929361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5615327,0.00008967581,0.4364752,0.00001985426,0.0000724393,0.00004794757,0.000004490166,0.000122167,0.00163553],"genre_scores_gemma":[0.9988807,0.00001129192,0.001061373,0.00001255098,0.00001079964,0.00000392487,0.00000437495,0.000006812571,0.000008124886],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.473741,"threshold_uncertainty_score":0.2928911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02918332286618001,"score_gpt":0.2433637331804426,"score_spread":0.2141804103142626,"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."}}