{"id":"W2172089762","doi":"10.1109/ccece.2005.1557253","title":"Aquantitative comparison of different mother wavelets for characterizing transients in power systems","year":2006,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Manitoba Hydro; University of Manitoba","funders":"","keywords":"Wavelet; Computer science; Wavelet transform; Signal processing; Power (physics); Electronic engineering; Electric power system; Time–frequency analysis; Pattern recognition (psychology); Artificial intelligence; Engineering; Telecommunications; Digital signal processing; 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":[0.00336439,0.0004648972,0.0005602727,0.002203506,0.0003041099,0.0009587685,0.0002976816,0.0006016854,0.0007144664],"category_scores_gemma":[0.01043424,0.000150925,0.0004829273,0.001596967,0.0003083325,0.001607583,0.0003744947,0.00048014,0.0002487139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002646952,"about_ca_system_score_gemma":0.0001942072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006163074,"about_ca_topic_score_gemma":0.0007015112,"domain_scores_codex":[0.9991611,0.0003491274,0.00005814452,0.00007664369,0.0002986066,0.0000563798],"domain_scores_gemma":[0.9964873,0.002245301,0.0001654896,0.0002895909,0.0007485301,0.00006393582],"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":[0.002179228,0.0002439501,0.0105745,0.0006661485,0.0002157186,0.0002169073,0.000624302,0.08861335,0.0771257,0.00962754,0.001914509,0.8079982],"study_design_scores_gemma":[0.0001251161,0.001238548,0.031041,0.0001720226,0.0003257974,0.0005251881,0.0007366894,0.8773704,0.07408314,0.005738257,0.008555663,0.00008816866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3315897,0.002862498,0.6583254,0.0003878284,0.0001916849,0.0001125997,0.0002557456,0.0005380586,0.005736304],"genre_scores_gemma":[0.718299,0.003168213,0.2762667,0.00006496757,0.00007623927,0.00007876204,0.000489076,0.0002066426,0.001350445],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00336439,"threshold_uncertainty_score":0.01779276,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03491446968939885,"score_gpt":0.3176982154792331,"score_spread":0.2827837457898342,"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."}}