{"id":"W4388020095","doi":"10.3390/su152115453","title":"Using Generic Direct M-SVM Model Improved by Kohonen Map and Dempster–Shafer Theory to Enhance Power Transformers Diagnostic","year":2023,"lang":"en","type":"article","venue":"Sustainability","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Direction Générale de la Recherche Scientifique et du Développement Technologique","keywords":"Dissolved gas analysis; Support vector machine; Self-organizing map; Data mining; Artificial intelligence; Dimensionality reduction; Transformer; Cluster analysis; Computer science; Machine learning; Residual; Dempster–Shafer theory; Curse of dimensionality; Rough set; Pattern recognition (psychology); Reliability engineering; Engineering; Algorithm","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":[0.0004910885,0.0002633522,0.0002561677,0.0001299258,0.0001241102,0.00005153051,0.0001194756,0.0001168718,0.00001969096],"category_scores_gemma":[0.0004307101,0.0002688504,0.00007323777,0.0004072839,0.00007074085,0.0002680946,0.00003188837,0.0001503439,0.00001092993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000417981,"about_ca_system_score_gemma":0.0000832099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006952811,"about_ca_topic_score_gemma":0.00001627781,"domain_scores_codex":[0.9984959,0.00004786732,0.000312733,0.0003888594,0.0001579393,0.0005967299],"domain_scores_gemma":[0.9990041,0.0003741596,0.00001878953,0.000259718,0.0001423021,0.0002009744],"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":[0.0003075304,0.0003091543,0.01096379,0.002975095,0.0003398094,0.00003408058,0.0158303,0.7097759,0.1023657,0.001158153,0.005678378,0.1502621],"study_design_scores_gemma":[0.0005007296,0.0001669917,0.01266723,0.00005797098,0.00009547776,0.000001604768,0.0006809868,0.9521375,0.01322782,0.01584552,0.00369442,0.0009237147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7936556,0.0004778252,0.2038226,0.0002690046,0.0001860163,0.0007814984,0.00009906122,0.0003134325,0.0003950179],"genre_scores_gemma":[0.9989849,0.0001513757,0.0004038535,0.00008896802,0.00002218638,0.0001112307,0.00003014038,0.00005956454,0.0001478041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2423616,"threshold_uncertainty_score":0.9999764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007938462400708906,"score_gpt":0.2557718660913827,"score_spread":0.2478334036906738,"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."}}