{"id":"W2134021005","doi":"10.1109/pes.2006.1708961","title":"Deriving asset probabilities of failure: effect of condition and maintenance levels","year":2006,"lang":"en","type":"article","venue":"2006 IEEE Power Engineering Society General Meeting","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kinectrics (Canada)","funders":"","keywords":"Asset (computer security); Risk analysis (engineering); Actuarial science; Life expectancy; Expectancy theory; Computer science; Focus (optics); Reliability engineering; Business; Economics; Engineering; Computer security","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":[0.0005284752,0.0002250418,0.0003444994,0.00004286086,0.00003886084,0.00002048312,0.00008197776,0.0001332838,0.000007742236],"category_scores_gemma":[0.00009828149,0.0002215232,0.0001438603,0.000181577,0.00007744772,0.0001796676,0.00001956584,0.0001445595,5.092841e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007727675,"about_ca_system_score_gemma":0.000009984642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005224112,"about_ca_topic_score_gemma":0.000006457612,"domain_scores_codex":[0.9988773,0.0000293037,0.0004235922,0.0001938032,0.0001633848,0.0003125556],"domain_scores_gemma":[0.9994575,0.0001673004,0.00008950198,0.0001626511,0.00008747489,0.00003558414],"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":[0.000002479318,0.000007163396,0.001278016,0.0007456851,0.00002908546,2.934496e-7,0.00015669,0.7310749,0.2656154,0.0002466849,0.0007386698,0.0001048875],"study_design_scores_gemma":[0.0005863222,0.00009770087,0.003720063,0.000503924,0.00003965518,0.000008481998,0.00006384266,0.5968866,0.3973082,0.0001300147,0.0002831056,0.0003721416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9361696,0.0003426948,0.06247645,0.00002338061,0.0002704135,0.0002446182,0.00003952116,0.0001829581,0.0002503126],"genre_scores_gemma":[0.9668015,0.00003536313,0.03294496,0.000005112635,0.00007652914,0.00002695713,0.00001426962,0.00004221956,0.00005304048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1341884,"threshold_uncertainty_score":0.9033455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002424704673448885,"score_gpt":0.1791650021014692,"score_spread":0.1767402974280204,"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."}}