{"id":"W4242156936","doi":"10.1002/asmb.790","title":"Optimal corrective maintenance contract planning for aging multi‐state system","year":2009,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Failure rate; Reliability engineering; Corrective maintenance; Computer science; Order (exchange); Optimal maintenance; Piecewise; Total cost; Function (biology); Mathematical optimization; Series (stratigraphy); Maintenance actions; State (computer science); Genetic algorithm; Operations research; Preventive maintenance; Engineering; Economics; Mathematics; Algorithm","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":[0.0001852324,0.0002127485,0.0002981485,0.00009296998,0.00008157404,0.00005085075,0.00008640104,0.0002405661,0.000001059734],"category_scores_gemma":[0.00003169234,0.0002088093,0.00002143179,0.000206836,0.00004212031,0.0002091105,0.0000145981,0.0003636319,5.112047e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001244266,"about_ca_system_score_gemma":0.00002549072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002026562,"about_ca_topic_score_gemma":0.000002030562,"domain_scores_codex":[0.9989605,0.000005863117,0.0002946144,0.000281714,0.0000846333,0.0003727007],"domain_scores_gemma":[0.9995722,0.00007469251,0.0000566537,0.0001328502,0.0001000845,0.00006355059],"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":[0.0000712875,0.00002135363,0.000009822454,0.00009427134,0.000008444291,0.000003275753,0.0005459739,0.9888102,0.0001519656,0.005015249,0.00003577722,0.005232363],"study_design_scores_gemma":[0.001388999,0.00001548755,0.001420008,0.0004332412,0.00001159133,0.00001130138,0.0006431909,0.9950355,0.0000442031,0.0007433172,0.000008457309,0.0002447641],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.110675,0.0001107779,0.8875491,0.00004202241,0.000191744,0.000576294,0.00001446493,0.0001455558,0.0006950261],"genre_scores_gemma":[0.9904253,0.00001293068,0.009243141,0.00004713261,0.00005666327,0.0001501249,0.00001279714,0.0000267474,0.00002516814],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8797503,"threshold_uncertainty_score":0.8514996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0162269215369855,"score_gpt":0.2268139392469692,"score_spread":0.2105870177099837,"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."}}