{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013301,0.0001927849,0.0003240471,0.00009994759,0.00003865735,0.00006396242,0.0002899123,0.0001254175,0.0001507736],"category_scores_gemma":[0.000213368,0.0001805074,0.0001934761,0.0001027704,0.00006558125,0.000734457,0.00003173744,0.000385759,0.000004087383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002522352,"about_ca_system_score_gemma":0.0000394916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003027337,"about_ca_topic_score_gemma":0.000003954833,"domain_scores_codex":[0.9982029,0.00006380541,0.0008944382,0.0001841446,0.0004229532,0.0002318071],"domain_scores_gemma":[0.9990525,0.0001667144,0.00010565,0.00017314,0.0003802759,0.0001217239],"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":[0.0001835172,0.00003627041,0.0001291241,0.00007400863,0.0001150168,0.000009017379,0.0003121837,0.9827586,0.0007880868,0.003224277,0.00009972656,0.0122701],"study_design_scores_gemma":[0.001596635,0.0001041808,0.006979444,0.0003128716,0.00004137199,0.0001994507,0.00007133765,0.9687544,0.001180576,0.01067031,0.00963217,0.0004572368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7480835,0.0004098119,0.2473966,0.0006614478,0.001289404,0.0001761944,0.00004592706,0.0001131932,0.001823878],"genre_scores_gemma":[0.9845219,0.003851636,0.01098572,0.00006545432,0.0003148073,0.000004929449,0.00001075995,0.00002507982,0.0002196666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2364384,"threshold_uncertainty_score":0.7360879,"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."}}