{"id":"W2048205884","doi":"10.1109/eic.2013.6554291","title":"Development of a generator prognostic tool","year":2013,"lang":"en","type":"article","venue":"","topic":"Power System Reliability and Maintenance","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro-Québec","funders":"","keywords":"Reliability engineering; Identification (biology); Root cause; Computer science; Condition-based maintenance; Condition monitoring; Prognostics; Context (archaeology); Generator (circuit theory); Root cause analysis; Maintenance engineering; Stator; Engineering; Power (physics); Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000520554,0.0000492929,0.00007533529,0.00001483566,0.000009128253,0.000006860103,0.00005411298,0.00002245444,0.0001953442],"category_scores_gemma":[0.00001760114,0.00003848022,0.00001442758,0.0000443609,0.00000741446,0.00005253825,0.00001124214,0.00002197995,0.0002466477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001395824,"about_ca_system_score_gemma":0.00001717222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005123031,"about_ca_topic_score_gemma":0.000006855389,"domain_scores_codex":[0.9996202,0.000003036751,0.0001661308,0.0000525219,0.00005464113,0.000103469],"domain_scores_gemma":[0.9998279,0.00001091326,0.000008184536,0.00009526807,0.0000331175,0.00002467539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006618243,0.0002752694,0.01886089,0.003282417,0.0003524971,0.000005162714,0.006857452,0.003801945,0.704747,0.01381307,0.08193331,0.1660644],"study_design_scores_gemma":[0.0006746716,0.00004661824,0.07283179,0.0002726801,0.00001486135,0.0000111849,0.0004522246,0.05613891,0.6627118,0.0004565126,0.2055537,0.0008350751],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9459212,0.000069225,0.04477539,0.00001280001,0.0001737409,0.0002286026,5.102696e-7,0.0001371511,0.008681416],"genre_scores_gemma":[0.9673647,0.000001451979,0.03220846,0.00001383299,0.00001052761,0.00007480758,6.017513e-7,0.000005990843,0.0003196352],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1652294,"threshold_uncertainty_score":0.3170239,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007263742950724285,"score_gpt":0.1740079529725113,"score_spread":0.166744210021787,"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."}}