{"id":"W3137048035","doi":"10.3390/en14071809","title":"Transformer Oil Quality Assessment Using Random Forest with Feature Engineering","year":2021,"lang":"en","type":"article","venue":"Energies","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Random forest; Computer science; C4.5 algorithm; Feature selection; Decision tree; Transformer; Transformer oil; Reliability engineering; Artificial intelligence; Extreme learning machine; Data mining; Machine learning; Naive Bayes classifier; Engineering; Support vector machine; Artificial neural network","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":[0.001639454,0.0008977455,0.0009688538,0.002490238,0.0003153897,0.0005789183,0.0004857671,0.0005456039,0.0007382639],"category_scores_gemma":[0.00257496,0.0002211862,0.001357785,0.001487507,0.0001800144,0.0008004683,0.0003796614,0.0004063121,0.0003128031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003014745,"about_ca_system_score_gemma":0.0005349047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005663119,"about_ca_topic_score_gemma":0.004738919,"domain_scores_codex":[0.9992968,0.0001633447,0.00006090219,0.0001848713,0.0001931454,0.0001010076],"domain_scores_gemma":[0.9990395,0.0004115963,0.000105747,0.00007904935,0.0003383595,0.00002575374],"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":[0.0006896975,0.0003111523,0.01350532,0.0001971795,0.0001774048,0.0002363707,0.00009068341,0.374939,0.02389053,0.001314469,0.00206835,0.5825799],"study_design_scores_gemma":[0.00001770935,0.00009425483,0.002838133,0.000007377104,0.00003527972,0.00005577319,0.00001616029,0.9912059,0.004478102,0.000816185,0.0004172336,0.00001791725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1081776,0.0002716362,0.8871425,0.00006070684,0.00003664872,0.0001258816,0.0004395377,0.00301851,0.0007268835],"genre_scores_gemma":[0.6995369,0.0001469355,0.2982854,0.00002764125,0.00002775409,0.0001466128,0.001233352,0.00008151533,0.0005139054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005663119,"threshold_uncertainty_score":0.01126033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01298555494098939,"score_gpt":0.2414031907440282,"score_spread":0.2284176358030388,"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."}}