{"id":"W2026381941","doi":"10.1061/41109(373)47","title":"A Decision Support System for Integrating Corrective Maintenance, Preventive Maintenance, and Condition-Based Maintenance","year":2010,"lang":"en","type":"article","venue":"","topic":"Manufacturing Process and Optimization","field":"Engineering","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Preventive maintenance; Corrective maintenance; Proactive maintenance; Maintenance engineering; Condition-based maintenance; Predictive maintenance; Reliability engineering; Computer science; Computerized maintenance management system; Risk analysis (engineering); Decision support system; Engineering; Business; Data mining","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":[0.0002928651,0.0002568426,0.0002856945,0.0001308505,0.0001501492,0.0000961485,0.0001378814,0.0001384645,0.000106491],"category_scores_gemma":[0.0001971447,0.000204975,0.00008121366,0.0001355086,0.00006825858,0.0002217008,0.00002339283,0.0002544798,0.00001047121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009202636,"about_ca_system_score_gemma":0.00004581199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001973497,"about_ca_topic_score_gemma":0.000181793,"domain_scores_codex":[0.998765,0.0000110947,0.0003529424,0.0003483134,0.000138949,0.0003836887],"domain_scores_gemma":[0.9990435,0.0002715822,0.00009968289,0.0002021999,0.000273701,0.0001093957],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001827427,0.0004336488,0.005165224,0.008348169,0.0006251681,0.0001044747,0.003472982,0.279948,0.01175153,0.1777849,0.155875,0.3546635],"study_design_scores_gemma":[0.0023792,0.0001637523,0.00189798,0.0007181193,0.00004640932,0.00004559186,0.001038208,0.9648392,0.01707651,0.001495761,0.009716679,0.0005826584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02840127,0.00001350214,0.9565924,0.00003260652,0.001308599,0.0007789563,0.00008623753,0.0004584441,0.01232796],"genre_scores_gemma":[0.9400303,0.00001020881,0.05806758,0.00007110428,0.00007457488,0.0002863066,0.00005921742,0.00005096952,0.001349742],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.911629,"threshold_uncertainty_score":0.8358639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003943451308184542,"score_gpt":0.2126231879979762,"score_spread":0.2086797366897916,"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."}}