{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004050201,0.000132747,0.0001289682,0.000115612,0.00009350895,0.00004073376,0.0000359497,0.00006524485,0.0001438804],"category_scores_gemma":[0.000005345817,0.0001304357,0.00004002031,0.00008766547,0.000008942342,0.0001493137,0.0000051524,0.00005760894,0.000003013883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004467004,"about_ca_system_score_gemma":0.000005050233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003193848,"about_ca_topic_score_gemma":0.000006942403,"domain_scores_codex":[0.9992017,0.00004104581,0.0002736354,0.0001781317,0.00008513817,0.0002203209],"domain_scores_gemma":[0.9997491,0.00004540085,0.00004271977,0.00008143306,0.00004056318,0.0000407359],"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":[0.0006253287,0.0003589435,0.01053976,0.003425134,0.001984647,0.00001791864,0.1678521,0.0321968,0.3070985,0.00005558854,0.00724085,0.4686044],"study_design_scores_gemma":[0.002070593,0.0004539399,0.0003886916,0.00008856777,0.0001277956,0.0001112944,0.005341383,0.7539277,0.2362263,0.0001842257,0.0003543986,0.0007250615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1092541,0.00002837561,0.8881864,7.964951e-7,0.0004108435,0.0006395876,0.00001117184,0.0002096184,0.001259123],"genre_scores_gemma":[0.9638801,0.000003117204,0.03581477,0.00001353393,0.00006500124,0.000163923,0.00001107743,0.0000384989,0.000009977898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.854626,"threshold_uncertainty_score":0.5319015,"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."}}