{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001195091,0.0008620966,0.001463971,0.0006558764,0.0006712269,0.001333866,0.0009284352,0.001326412,0.001825848],"category_scores_gemma":[0.002812097,0.0008820688,0.0005956313,0.0003425884,0.001046783,0.0009101112,0.000834605,0.0007787218,0.000182275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001141126,"about_ca_system_score_gemma":0.001738954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009336054,"about_ca_topic_score_gemma":0.008419993,"domain_scores_codex":[0.9995239,0.0001571621,0.00002361661,0.0001093134,0.00008520792,0.000100921],"domain_scores_gemma":[0.9987685,0.0007177111,0.0001915978,0.00004872852,0.0001978735,0.00007558738],"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":[0.0001551051,0.00002745586,0.0003154409,0.00004295021,0.00002847374,0.00005775312,0.00003526885,0.9902844,0.002854122,0.00149324,0.0001148491,0.004590858],"study_design_scores_gemma":[0.00001957104,0.00005246654,0.000148368,0.000002580977,0.00001646472,0.00000524274,0.000006486558,0.9987605,0.0003647244,0.0005714866,0.00004853422,0.000003653177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3694721,0.0006285625,0.6237181,0.0007130944,0.00007013319,0.0002391038,0.000250286,0.0004637123,0.00444483],"genre_scores_gemma":[0.9865303,0.00006864245,0.0122524,0.00002436086,0.0000125489,0.00006729046,0.00004440774,0.0000166138,0.0009833819],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009336054,"threshold_uncertainty_score":0.01856345,"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."}}