{"id":"W2145487113","doi":"10.1109/tdei.2009.4815187","title":"A cascade of artificial neural networks to predict transformers oil parameters","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Dielectrics and Electrical Insulation","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Cascade; Artificial neural network; Transformer; Voltage; Transformer oil; Backpropagation; Computer science; Biological system; Engineering; Artificial intelligence; Electronic engineering; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001223398,0.001109685,0.0007290909,0.0006558998,0.0003617317,0.0005013078,0.001116535,0.0007203528,0.001656236],"category_scores_gemma":[0.002400237,0.000596859,0.0007196883,0.0004131526,0.0002633666,0.001080803,0.000531456,0.001024729,0.0005883228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007027779,"about_ca_system_score_gemma":0.0005513508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008375636,"about_ca_topic_score_gemma":0.008074042,"domain_scores_codex":[0.9995537,0.00008536127,0.00002846261,0.0001067003,0.0001607012,0.00006497052],"domain_scores_gemma":[0.9992448,0.0002700262,0.00005442885,0.00006812093,0.000324207,0.00003840707],"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":[0.0005026831,0.0002370796,0.006369636,0.0001056305,0.0001994226,0.0002703663,0.00008326707,0.7845408,0.01260387,0.00148836,0.00145844,0.1921403],"study_design_scores_gemma":[0.00000474737,0.00004975522,0.0004761726,0.000005736244,0.00001604873,0.00001335806,0.000004087831,0.9964735,0.002435064,0.000287619,0.0002268929,0.000006974217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2624373,0.00105034,0.7265897,0.0003145785,0.0002969611,0.0001727574,0.0002597279,0.002387326,0.006491316],"genre_scores_gemma":[0.8581035,0.0003678774,0.1364345,0.00009144904,0.00004681936,0.00009847007,0.0002864393,0.00004704539,0.004523847],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008375636,"threshold_uncertainty_score":0.01665378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01195329314194863,"score_gpt":0.2223310098787291,"score_spread":0.2103777167367805,"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."}}