{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004740672,0.0000517901,0.00006738575,0.0000686836,0.00002447939,0.000005283049,0.00003236004,0.00003671105,0.00000233014],"category_scores_gemma":[0.00001329088,0.00004688967,0.00003062866,0.0000959268,0.000007754343,0.00008448145,0.000003831964,0.0000208937,0.000002549307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009886802,"about_ca_system_score_gemma":0.000005898447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002368091,"about_ca_topic_score_gemma":0.000003153009,"domain_scores_codex":[0.9996737,0.000002045693,0.0001098179,0.00005326393,0.00006749092,0.00009371537],"domain_scores_gemma":[0.9998399,0.0000383705,0.0000108825,0.00007080923,0.00002464023,0.00001541908],"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":[0.000005953118,0.00000394409,0.00007920549,0.00006226257,0.000009421891,1.032569e-7,0.0003613739,0.9257659,0.06476468,0.001523516,0.004250614,0.003173018],"study_design_scores_gemma":[0.0001902409,0.00001709014,0.001698395,0.00003435415,0.0000071203,8.38797e-8,0.00004186734,0.917949,0.077571,0.00149195,0.0009583369,0.00004059895],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9212651,0.0001212627,0.07642917,0.00003182953,0.0003292871,0.00009559706,0.0001627528,0.0003259557,0.001239059],"genre_scores_gemma":[0.9983859,0.0004297538,0.0007706289,0.000002707407,0.00003073533,0.00002802149,0.0001427266,0.00001516237,0.0001943654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07712082,"threshold_uncertainty_score":0.1912105,"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."}}