{"id":"W4392567468","doi":"10.2139/ssrn.4743296","title":"Data-Driven Fault Prediction in Power Transformers: An Industrial Case Study","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Transformer; Reliability engineering; Fault (geology); Computer science; Electrical engineering; Engineering; Voltage; Seismology; Geology","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001600272,0.000399872,0.0003784544,0.0005113243,0.00008583179,0.0002464169,0.000489897,0.0004557362,0.00003576053],"category_scores_gemma":[0.00002265312,0.0003981791,0.0001036565,0.0002582214,0.00002027478,0.0004771039,0.0001074374,0.009208191,0.00001796084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001582867,"about_ca_system_score_gemma":0.001622828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004954279,"about_ca_topic_score_gemma":0.01349012,"domain_scores_codex":[0.9966925,0.00009293018,0.0007731782,0.0004880027,0.0003870763,0.00156632],"domain_scores_gemma":[0.9992661,0.00003327439,0.00006535873,0.0004493307,0.00004997783,0.0001359889],"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":[0.0003538262,0.002844684,0.01154603,0.0004206499,0.006147874,0.006796052,0.02119654,0.6800587,0.0003029039,0.003413071,0.00158436,0.2653353],"study_design_scores_gemma":[0.009016118,0.004274931,0.001475547,0.001106322,0.001941221,0.01400702,0.04608955,0.8424769,0.00004706664,0.07257186,0.004468573,0.002524889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9853393,0.00222548,0.007003444,0.00007005746,0.003099555,0.000883596,0.0005428738,0.0002020269,0.0006337125],"genre_scores_gemma":[0.9959183,0.002820485,0.00002120868,0.000005361893,0.0007093876,0.00003334195,0.00036687,0.000100045,0.00002504293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2628104,"threshold_uncertainty_score":0.999847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03705867889769097,"score_gpt":0.2858901982402612,"score_spread":0.2488315193425702,"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."}}