{"id":"W2074236332","doi":"10.1016/j.ress.2015.03.029","title":"Optimal preventive maintenance and repair policies for multi-state systems","year":2015,"lang":"en","type":"article","venue":"Reliability Engineering & System Safety","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":73,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Imperfect; Schedule; Preventive maintenance; Reliability engineering; State (computer science); Optimal maintenance; State space; Mathematical optimization; Markov process; Computer science; Homogeneous; Operations research; Engineering; Mathematics; Algorithm; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00264176,0.001339721,0.001884591,0.001484414,0.0006677856,0.00160591,0.001447882,0.001563439,0.002325747],"category_scores_gemma":[0.009339715,0.001218067,0.0007961148,0.0007669934,0.001559758,0.002043208,0.001321521,0.001523039,0.0002190117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002246946,"about_ca_system_score_gemma":0.002144828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008094404,"about_ca_topic_score_gemma":0.006287831,"domain_scores_codex":[0.9989574,0.00033007,0.00005996776,0.0001935571,0.000147477,0.0003115301],"domain_scores_gemma":[0.992268,0.005948386,0.0007393755,0.0002594599,0.0004633412,0.0003214102],"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":[0.0002052826,0.0000657739,0.0002804535,0.00004772929,0.00003616673,0.0000334126,0.00003067327,0.9884952,0.0008334643,0.004334738,0.0003099172,0.00532731],"study_design_scores_gemma":[0.00002326243,0.00003731893,0.000239457,0.000005227918,0.00001524005,0.000008008481,0.0000103843,0.9952978,0.0002130742,0.004096947,0.00004735608,0.000005963926],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3644869,0.001523694,0.6258532,0.001566277,0.0001426356,0.0001697534,0.0004213112,0.0005880229,0.005248221],"genre_scores_gemma":[0.9851962,0.0002247285,0.01260736,0.00005199348,0.00004243046,0.00005050186,0.00008671428,0.00003643123,0.001703606],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008094404,"threshold_uncertainty_score":0.01630282,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01375373015587376,"score_gpt":0.2218422613420256,"score_spread":0.2080885311861518,"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."}}