{"id":"W2137704118","doi":"10.1109/tr.2013.2241196","title":"Comparative Analysis of Optimal Maintenance Policies Under General Repair With Underlying Weibull Distributions","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Reliability","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Health Services and Policy Research","funders":"","keywords":"Weibull distribution; Preventive maintenance; Mathematical optimization; Monte Carlo method; Function (biology); Reliability engineering; Computer science; Maintenance engineering; Mathematics; Applied mathematics; Engineering; 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":[0.004534269,0.0006157996,0.001105696,0.001705275,0.0003634875,0.0008794218,0.0008622372,0.0009225963,0.001413326],"category_scores_gemma":[0.01432927,0.000329958,0.0004829579,0.001015981,0.0006875582,0.001318606,0.0004750553,0.0005768712,0.0001313698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001805499,"about_ca_system_score_gemma":0.001582671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001786002,"about_ca_topic_score_gemma":0.001508502,"domain_scores_codex":[0.9991227,0.0004067959,0.00003450084,0.00009183111,0.0001588827,0.0001853073],"domain_scores_gemma":[0.9877787,0.009705207,0.001011627,0.0004275611,0.0007975071,0.0002794242],"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":[0.0003417358,0.00006531509,0.001179299,0.0001089301,0.00004394844,0.00004672865,0.00006324569,0.9667613,0.001546505,0.01640918,0.0004125746,0.01302132],"study_design_scores_gemma":[0.00003181072,0.0002386776,0.001592528,0.0000204237,0.00005945672,0.00004316013,0.00008149006,0.9895059,0.001300054,0.006785957,0.0003262995,0.0000141151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7642761,0.003455957,0.2229998,0.0004926451,0.00005608588,0.0001015753,0.0002196722,0.0003208065,0.008077384],"genre_scores_gemma":[0.9867491,0.0006280037,0.01200819,0.00001905054,0.00001353518,0.00004332474,0.00006592998,0.0000277514,0.0004450785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004534269,"threshold_uncertainty_score":0.02397984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01723559893983237,"score_gpt":0.2512624210522543,"score_spread":0.234026822112422,"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."}}