{"id":"W1484098979","doi":"10.1016/j.neucom.2015.05.112","title":"Modeling and recognition of smart grid faults by a combined approach of dissimilarity learning and one-class classification","year":2015,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Smart grid; Computer science; Grid; Fault (geology); Electric power system; Data mining; Transformer; Class (philosophy); Artificial intelligence; Electricity; Fuzzy logic; Machine learning; Power (physics); 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":[0.0003005476,0.00007933102,0.0001434836,0.00006414136,0.0000977735,0.00002887034,0.0001263792,0.00005183172,1.827979e-7],"category_scores_gemma":[0.00004607117,0.00008566035,0.00002028867,0.0002087396,0.00004008537,0.0001533123,0.0001455205,0.0001512069,2.239634e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009835437,"about_ca_system_score_gemma":0.00001875634,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000344825,"about_ca_topic_score_gemma":6.148007e-7,"domain_scores_codex":[0.9991466,0.00006980429,0.0002682503,0.0002794303,0.0001364632,0.00009944187],"domain_scores_gemma":[0.9994442,0.00004931317,0.0001773406,0.0001322653,0.0001303968,0.00006651217],"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":[0.0001536083,0.001314429,0.02238444,0.0007203593,0.00006995281,0.000001315116,0.005165731,0.007401706,0.1435013,0.02672454,0.0004381034,0.7921245],"study_design_scores_gemma":[0.0002371386,0.0001427481,0.000745111,0.00002268169,0.000006379235,0.00000600468,0.00008481176,0.9928536,0.004101998,0.00164139,0.0000780781,0.00008005805],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.383265,0.00002034524,0.6160255,0.00009922261,0.00001451634,0.0001268102,0.00000161044,0.00008358644,0.0003634417],"genre_scores_gemma":[0.9298726,0.00001534459,0.07004574,0.00002072671,0.00001430726,0.00001040125,0.000007202299,0.000006059031,0.000007555403],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9854519,"threshold_uncertainty_score":0.3493128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06051662701551754,"score_gpt":0.2661619132694653,"score_spread":0.2056452862539477,"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."}}