{"id":"W2287529450","doi":"10.1049/el.2015.2957","title":"Fast identification of partial discharge sources using blind source separation and kurtosis","year":2015,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Kurtosis; Blind signal separation; Identification (biology); Separation (statistics); Source separation; Computer science; Algorithm; Mathematics; Telecommunications; Statistics; Machine learning","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":[0.0006766294,0.000121099,0.0001436043,0.0001535301,0.00008666224,0.0001916328,0.000307718,0.00005937207,0.000001144637],"category_scores_gemma":[0.00003405226,0.0001255046,0.00003947024,0.0003001164,0.00006135908,0.00069338,0.00008959636,0.0001394195,0.000004737579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007129955,"about_ca_system_score_gemma":0.00009654069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002422164,"about_ca_topic_score_gemma":0.000006768194,"domain_scores_codex":[0.9986942,0.0001355838,0.0003297078,0.0002965126,0.0003060658,0.0002378936],"domain_scores_gemma":[0.9992245,0.00003186714,0.000263958,0.0003215602,0.00008295909,0.00007517316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000580468,0.00013773,0.002955068,0.00002711185,0.00007111654,0.000001326881,0.01389256,0.01166086,0.9201186,0.04248419,0.001704001,0.006889424],"study_design_scores_gemma":[0.0007469926,0.0001633204,0.0003872818,0.00001684595,0.00003166122,0.00002531539,0.0001119915,0.3885991,0.603084,0.00155358,0.004923571,0.0003563452],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4834479,0.0001426587,0.5148576,0.001320977,0.00003518514,0.0001035816,6.469654e-7,0.00007539733,0.00001605568],"genre_scores_gemma":[0.9911016,0.00001579913,0.008240406,0.0005256807,0.00005138698,0.00001161387,0.00000694764,0.00001178227,0.00003481565],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5076537,"threshold_uncertainty_score":0.5117929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02472799852238328,"score_gpt":0.2890817200638551,"score_spread":0.2643537215414718,"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."}}