{"id":"W4388832859","doi":"10.3390/en16227656","title":"Stacked Ensemble Regression Model for Prediction of Furan","year":2023,"lang":"en","type":"article","venue":"Energies","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Generalizability theory; Computer science; Machine learning; Reliability engineering; Furan; Reliability (semiconductor); Regression; Cross-validation; Training set; Data mining; Artificial intelligence; Power (physics); Engineering; Statistics; Mathematics","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":[0.001572815,0.001347535,0.001187044,0.000801841,0.0003226296,0.0008309172,0.001131322,0.0009043533,0.001512669],"category_scores_gemma":[0.002749084,0.0003377911,0.001276414,0.0008762957,0.0002393284,0.0008271501,0.0005546858,0.00156933,0.0007522663],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004692473,"about_ca_system_score_gemma":0.0007163419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01638586,"about_ca_topic_score_gemma":0.01130442,"domain_scores_codex":[0.9995135,0.0001611719,0.00002674445,0.0001431623,0.00008979407,0.00006568849],"domain_scores_gemma":[0.9990312,0.0005198243,0.00009434068,0.00007915551,0.0002473026,0.00002817407],"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":[0.0001205951,0.00007702872,0.005015082,0.00004155138,0.00013353,0.00007033726,0.00003067113,0.9409368,0.001718151,0.0009088943,0.001577156,0.04937012],"study_design_scores_gemma":[0.000001273906,0.00001166305,0.0004172381,0.000002610405,0.000008069036,0.000004952172,0.000003587935,0.9989931,0.0001824523,0.0002560133,0.0001145988,0.000004388619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2517303,0.002399125,0.7390503,0.0005538028,0.0002680999,0.00007406363,0.001360862,0.001674477,0.002888989],"genre_scores_gemma":[0.9432375,0.000821855,0.04936207,0.0001260399,0.0001023639,0.0001032619,0.002030583,0.00007291261,0.004143555],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01638586,"threshold_uncertainty_score":0.03258097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02565956272607766,"score_gpt":0.2375111853535629,"score_spread":0.2118516226274852,"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."}}