{"id":"W2170749669","doi":"10.1109/pes.2011.6038901","title":"A pattern recognition approach for detecting power islands using transient signals — Part I: Design and implementation","year":2011,"lang":"en","type":"article","venue":"","topic":"Islanding Detection in Power Systems","field":"Engineering","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Islanding; Transient (computer programming); Pattern recognition (psychology); Computer science; Wavelet transform; Artificial intelligence; Decision tree; Voltage; Transient voltage suppressor; Waveform; Feature vector; Categorization; Classifier (UML); Wavelet; Electronic engineering; Electric power system; Power (physics); Engineering; Radar; Telecommunications; Physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002970559,0.0004770688,0.0004155538,0.0005671667,0.0002020345,0.0007415928,0.001108851,0.0006249541,0.002836436],"category_scores_gemma":[0.0007860033,0.0003247042,0.0002891391,0.0005862807,0.0002933972,0.0008077106,0.0002005514,0.0005085885,0.001577275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002836256,"about_ca_system_score_gemma":0.0003890145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001039079,"about_ca_topic_score_gemma":0.000947405,"domain_scores_codex":[0.9997249,0.00003164845,0.00002488289,0.00007192333,0.0001284339,0.00001805141],"domain_scores_gemma":[0.9996828,0.00008116967,0.00003457968,0.00004168219,0.0001466806,0.00001308912],"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":[0.0001458652,0.0001350377,0.0007885174,0.0002240666,0.00005269051,0.0001764969,0.00006002355,0.01073572,0.1813576,0.004511348,0.002495877,0.7993168],"study_design_scores_gemma":[0.0001247416,0.001060204,0.003490085,0.00005903163,0.0001151543,0.001546722,0.00006212151,0.7746477,0.1912151,0.003520883,0.02409182,0.00006647342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004825027,0.0001066803,0.9929489,0.00006833834,0.00004384729,0.0001018258,0.0000275684,0.000961981,0.0009158187],"genre_scores_gemma":[0.1473504,0.0003374472,0.8477046,0.0001473561,0.00006904755,0.0004436525,0.0002085692,0.00006539554,0.003673387],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002836436,"threshold_uncertainty_score":0.009488821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08155512355324185,"score_gpt":0.2617651983381182,"score_spread":0.1802100747848763,"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."}}