{"id":"W2061758728","doi":"10.1016/j.jspi.2012.02.045","title":"Optimal design and maintenance of a repairable multi-state system with standby components","year":2012,"lang":"en","type":"article","venue":"Journal of Statistical Planning and Inference","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reliability engineering; Markov model; Markov process; Mathematical optimization; State (computer science); Markov chain; Process (computing); Mathematics; Electric power system; Work (physics); Power (physics); Preventive maintenance; Reliability (semiconductor); Control theory (sociology); Computer science; Engineering; Statistics; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000414015,0.00008885584,0.0002319962,0.00004365557,0.00003204788,0.00002085505,0.00003798485,0.00003073964,0.000002232021],"category_scores_gemma":[0.0001296103,0.0000627158,0.000009854738,0.00004735237,0.00009235734,0.0002370114,0.00001032932,0.0001449129,2.970192e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002860481,"about_ca_system_score_gemma":0.00001589425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000724429,"about_ca_topic_score_gemma":1.713377e-7,"domain_scores_codex":[0.9993111,0.00003610327,0.0002988598,0.00005839013,0.0001209715,0.0001745816],"domain_scores_gemma":[0.9993415,0.0002571738,0.0001135727,0.00004912915,0.0001153854,0.0001231662],"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":[0.0004861128,0.00006924859,0.02374466,0.0007367862,0.00008560815,0.00004277854,0.001716519,0.9677255,0.001104986,0.002503132,0.0003425078,0.001442119],"study_design_scores_gemma":[0.0008790818,0.0004102748,0.02989559,0.001196723,0.00003561751,0.0001957639,0.0004886751,0.9663562,0.0002820684,0.00005921598,0.0000636239,0.0001371261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1604003,0.0004035979,0.8389986,0.0000045473,0.00004908177,0.00005445799,0.00001486812,0.00001423591,0.00006027948],"genre_scores_gemma":[0.7457467,0.00009832686,0.2541296,0.000002906037,0.000008120916,8.979792e-7,9.85521e-7,0.000005922335,0.000006494824],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5853464,"threshold_uncertainty_score":0.2557476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02763495094561653,"score_gpt":0.2504595994827245,"score_spread":0.222824648537108,"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."}}