{"id":"W2005362891","doi":"10.1142/s0218539300000213","title":"GENERAL SEQUENTIAL IMPERFECT PREVENTIVE MAINTENANCE MODELS","year":2000,"lang":"en","type":"article","venue":"International Journal of Reliability Quality and Safety Engineering","topic":"Reliability and Maintenance Optimization","field":"Engineering","cited_by":176,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Alberta; University of New Brunswick","funders":"University of Hong Kong; University of Windsor; City University of Hong Kong","keywords":"Weibull distribution; Imperfect; Preventive maintenance; Schedule; Hazard ratio; Hazard; Statistics; Reliability engineering; Reduction (mathematics); Computer science; Mathematics; Econometrics; Engineering; Confidence interval; Biology","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":[0.001218404,0.001375621,0.001286514,0.0008342103,0.0003899515,0.001186523,0.003517639,0.001343763,0.008195108],"category_scores_gemma":[0.00262923,0.000766004,0.001066956,0.0009510081,0.0009466201,0.002100287,0.0009622873,0.001329615,0.00106139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001058821,"about_ca_system_score_gemma":0.001001931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006995547,"about_ca_topic_score_gemma":0.004756671,"domain_scores_codex":[0.9991046,0.0001622062,0.00005332827,0.0002331539,0.0002798383,0.0001667749],"domain_scores_gemma":[0.9986674,0.0004833547,0.0003661811,0.0001738058,0.0002079417,0.0001013135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009658456,0.0000536739,0.0005956696,0.00009554903,0.0000356381,0.0001295896,0.00006116911,0.9441948,0.0009584089,0.04132109,0.001554517,0.01090334],"study_design_scores_gemma":[0.00002441664,0.00005108144,0.0002576022,0.000008913133,0.0000276398,0.00006638453,0.00001002254,0.9778709,0.0002311983,0.01916631,0.002272676,0.00001273577],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05412094,0.001046769,0.9209729,0.0005587226,0.0002097519,0.0001248168,0.001192047,0.0006224661,0.02115161],"genre_scores_gemma":[0.8924771,0.001448804,0.06391245,0.0002135305,0.0002342518,0.0002806596,0.0008572114,0.0001275572,0.04044834],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008195108,"threshold_uncertainty_score":0.02741534,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01041670103590322,"score_gpt":0.248793112411156,"score_spread":0.2383764113752528,"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."}}