{"id":"W4385487213","doi":"10.1109/actea58025.2023.10194101","title":"Transformers Faults Prediction Using Machine Learning Approach","year":2023,"lang":"en","type":"article","venue":"","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"Université du Québec à Rimouski","keywords":"Transformer; Computer science; Decision tree; Machine learning; Reliability engineering; Voltage; Artificial intelligence; Data mining; Engineering; Electrical engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00009777219,0.00009659699,0.00008085935,0.0001339732,0.00007654355,0.00001507602,0.00003765538,0.00005805814,0.00003508709],"category_scores_gemma":[0.000006379902,0.00009387157,0.00004073953,0.000356777,0.000009461355,0.0001774824,0.000002984499,0.000130448,0.00003948794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003483463,"about_ca_system_score_gemma":0.000005282793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003980451,"about_ca_topic_score_gemma":0.000005059268,"domain_scores_codex":[0.9994179,0.000006618888,0.0001461075,0.0001017139,0.0001217205,0.0002059413],"domain_scores_gemma":[0.9998685,0.00001805585,0.000006505266,0.00004609281,0.00001254012,0.00004827773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003686688,0.00001123903,0.004179177,0.00006931428,0.00003506972,0.000001145008,0.0004162907,0.9774118,0.01147315,0.0001983042,0.000111428,0.00608939],"study_design_scores_gemma":[0.000234105,0.00001685012,0.004366011,0.000009734405,0.0000139279,0.000002769112,0.00006882918,0.989278,0.002214636,0.00003290372,0.003659813,0.0001024539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5476795,0.00008881307,0.4047686,0.00001996927,0.0003920916,0.000195095,0.00003218175,0.00172969,0.04509402],"genre_scores_gemma":[0.9983866,0.0003091564,0.0008692868,0.000007339755,0.00004416196,0.000008129632,0.0002183755,0.00002997259,0.0001270158],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.450707,"threshold_uncertainty_score":0.3827971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01860273031341241,"score_gpt":0.2178133335992008,"score_spread":0.1992106032857884,"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."}}