{"id":"W2960944079","doi":"10.3390/en12142694","title":"Application of Machine Learning in Transformer Health Index Prediction","year":2019,"lang":"en","type":"article","venue":"Energies","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; École de Technologie Supérieure","funders":"","keywords":"Transformer; Reliability engineering; Test set; Computer science; Transformer oil; Machine learning; Engineering; Voltage; Artificial intelligence; Data mining; 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":[0.00007823434,0.0000543714,0.0000937081,0.00009316784,0.00001040759,0.000003611155,0.00003147411,0.00003371755,0.0000141652],"category_scores_gemma":[0.00000241676,0.00005596224,0.00001791522,0.0001237283,0.000005970153,0.000103737,0.000001243213,0.00008298519,0.000005877427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003261457,"about_ca_system_score_gemma":0.000009492188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000224842,"about_ca_topic_score_gemma":0.00008538075,"domain_scores_codex":[0.9995905,0.000007139236,0.0001723094,0.00006211893,0.00007439146,0.00009357881],"domain_scores_gemma":[0.9998832,0.00001838016,0.00001764717,0.00005638685,0.000008865061,0.00001553182],"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":[0.000004431796,0.00001101674,0.09821693,0.00008100444,0.000005638012,3.042335e-8,0.000531237,0.8729568,0.007009821,0.0007781083,0.00001047083,0.02039449],"study_design_scores_gemma":[0.0004127392,0.00006187239,0.2471393,0.00004142346,0.000002139275,3.992612e-7,0.00007060789,0.7353126,0.008992898,0.0001437795,0.00774676,0.00007545365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9633835,0.00068493,0.03256161,0.00005444792,0.0001343793,0.0001495225,0.0000119403,0.0001044266,0.002915254],"genre_scores_gemma":[0.9991247,0.0006654029,0.00008204807,0.000009429138,0.00001192373,0.00001290938,0.00005873556,0.00001121602,0.0000236507],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1489224,"threshold_uncertainty_score":0.2282074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003880417165315889,"score_gpt":0.1988577170019985,"score_spread":0.1949772998366826,"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."}}