{"id":"W4415221762","doi":"10.1109/tii.2025.3613710","title":"Nonintrusive Anomaly Detection of Users’ Reactive Power Compensators Using Metering Data","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Electricity Theft Detection Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Metering mode; AC power; Transformer; Exploit; Software deployment; Fault detection and isolation; Electricity; Fault (geology)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002591784,0.0002197362,0.0003177698,0.0007691812,0.0001440209,0.00004832504,0.0003377782,0.0003113026,0.0000224435],"category_scores_gemma":[0.0000279885,0.0002466992,0.00008321748,0.0009964937,0.00006113819,0.0007525564,0.000006533848,0.0006978228,0.000006013792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003572027,"about_ca_system_score_gemma":0.00007884803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00014533,"about_ca_topic_score_gemma":0.00003292123,"domain_scores_codex":[0.9986818,0.00004046127,0.0007156223,0.0001201166,0.0002074717,0.0002345664],"domain_scores_gemma":[0.9989305,0.000162952,0.0001598658,0.000592077,0.000102899,0.0000516631],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000581433,0.0002605116,0.00005976706,0.0003209314,0.001855301,0.000004427233,0.003109607,0.470669,0.3697502,0.0001039217,0.0004678555,0.1528171],"study_design_scores_gemma":[0.0004682773,0.00012276,0.000009296361,0.0001128433,0.0001058483,0.000007935067,0.0004109414,0.1664182,0.8316677,0.00002386709,0.000467933,0.0001844228],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3810622,0.00000493096,0.6165292,0.000004529146,0.0009405199,0.0002855714,0.00007276041,0.0003213667,0.0007789434],"genre_scores_gemma":[0.9971499,0.00001650954,0.002720275,0.00002468659,0.000026609,0.00001317936,0.000004160533,0.0000252308,0.000019475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6160876,"threshold_uncertainty_score":0.9999985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05125269059843968,"score_gpt":0.2729612473409045,"score_spread":0.2217085567424649,"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."}}