{"id":"W4401326743","doi":"10.1109/access.2024.3439248","title":"Enhancing Transformer Health Index Prediction Using Dissolved Gas Analysis Data Through Integration of LightGBM and Robust EM Algorithms","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Saskatchewan Research Council","keywords":"Missing data; Computer science; Imputation (statistics); Data mining; Boosting (machine learning); Dissolved gas analysis; Computation; Expectation–maximization algorithm; Maximization; Data modeling; Transformer; Algorithm; Artificial intelligence; Machine learning; Statistics; Maximum likelihood; Engineering; Mathematics; Mathematical optimization; Transformer oil","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001426473,0.001005874,0.001094335,0.001020576,0.0002104871,0.0006511531,0.0009748302,0.0007002025,0.0007256641],"category_scores_gemma":[0.002667577,0.0003591075,0.0008620872,0.0006706447,0.0002686526,0.0008945437,0.000734737,0.0009283092,0.0005312564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003520227,"about_ca_system_score_gemma":0.0005762271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002656302,"about_ca_topic_score_gemma":0.002836966,"domain_scores_codex":[0.9996386,0.00009935527,0.00002150334,0.00009544612,0.0001036393,0.00004153766],"domain_scores_gemma":[0.9993606,0.0002771311,0.00007004874,0.00006493666,0.0002028049,0.00002451816],"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":[0.0002700345,0.0002536106,0.008352429,0.0001603369,0.0001731453,0.0001271765,0.00007938159,0.5699242,0.01546825,0.001911817,0.003219337,0.4000603],"study_design_scores_gemma":[0.000005141679,0.0000188979,0.0005244651,0.00000513474,0.000009475989,0.00001509226,0.000006659121,0.9962841,0.002163302,0.0006023903,0.0003600742,0.000005306958],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03863562,0.0004338148,0.9578578,0.0001915539,0.00007202142,0.00003321531,0.0001310369,0.001658647,0.0009862159],"genre_scores_gemma":[0.6739698,0.0003351845,0.3218743,0.0002808082,0.0001161944,0.00008988752,0.0008249987,0.000187225,0.002321703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002656302,"threshold_uncertainty_score":0.007543981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06293758002059877,"score_gpt":0.326979939128666,"score_spread":0.2640423591080672,"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."}}