{"id":"W2616008120","doi":"","title":"Determining appropriate data analytics for transformer health monitoring","year":2017,"lang":"en","type":"book-chapter","venue":"Strathprints: The University of Strathclyde institutional repository (University of Strathclyde)","topic":"Power Transformer Diagnostics and Insulation","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kinectrics (Canada); Bruce Power (Canada)","funders":"","keywords":"Prognostics; Analytics; Anomaly detection; Reliability engineering; Software deployment; Suite; Engineering; Transformer; Systems engineering; Condition monitoring; Computer science; Risk analysis (engineering); Data science; Data mining; Software engineering; Electrical engineering","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":[0.001459235,0.0009193048,0.0005307192,0.001819158,0.0003378601,0.004084897,0.001357083,0.000703955,0.004127546],"category_scores_gemma":[0.00297199,0.0005831617,0.0004980051,0.002191807,0.0006386468,0.004529328,0.0011315,0.001712494,0.00384133],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009998602,"about_ca_system_score_gemma":0.001147066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007642432,"about_ca_topic_score_gemma":0.001009486,"domain_scores_codex":[0.9991382,0.0001221542,0.00006048167,0.0001315719,0.0005095454,0.00003800932],"domain_scores_gemma":[0.9986523,0.0007357175,0.00005490646,0.0001436918,0.0003723949,0.00004113049],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004986638,0.00009564967,0.001378821,0.001066055,0.00002809498,0.0004898391,0.0006637728,0.01130605,0.0219187,0.2497303,0.04069425,0.6725786],"study_design_scores_gemma":[0.00001795913,0.00008251246,0.001451477,0.001237709,0.00005038982,0.0011352,0.0005469586,0.126788,0.04950142,0.1496241,0.6694937,0.00007059886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007916426,0.007914781,0.9079606,0.00216225,0.0004859165,0.0004452211,0.001134192,0.003907101,0.06807356],"genre_scores_gemma":[0.04315037,0.01415948,0.8908508,0.0005692726,0.0002727233,0.0003234786,0.002863354,0.001185047,0.04662554],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004127546,"threshold_uncertainty_score":0.01380795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05426263444798051,"score_gpt":0.2314474316331097,"score_spread":0.1771847971851292,"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."}}