{"id":"W2347129034","doi":"10.1002/prs.11829","title":"Dynamic risk‐based maintenance for offshore processing facility","year":2016,"lang":"en","type":"article","venue":"Process Safety Progress","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Engineering; Reliability engineering; Corrective maintenance; Preventive maintenance; Bayesian network; Planned maintenance; Failure mode, effects, and criticality analysis; Risk analysis (engineering); Predictive maintenance; Operations research; Computer science; Failure mode and effects analysis","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":[0.004611217,0.0003926908,0.0007452434,0.0002813164,0.0007849161,0.000272636,0.001587724,0.0001928275,0.0001731092],"category_scores_gemma":[0.004458746,0.0002138858,0.0004639042,0.001409801,0.0006437828,0.000818447,0.0001105314,0.0001928461,0.0001564282],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001433747,"about_ca_system_score_gemma":0.0003960431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004408136,"about_ca_topic_score_gemma":0.00007843626,"domain_scores_codex":[0.9945031,0.0002561532,0.001303201,0.001400735,0.001691548,0.0008453051],"domain_scores_gemma":[0.9946752,0.001273967,0.0008854007,0.0009840725,0.00191472,0.0002666713],"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":[0.0007764896,0.0001381926,0.0813017,0.00009056865,0.00002962365,0.000002417,0.0002245691,0.0004198909,0.00002972998,0.0001061931,0.0003184252,0.9165622],"study_design_scores_gemma":[0.009016003,0.0006334513,0.1377972,0.0008472011,0.0004645397,0.0000237543,0.00216145,0.3761333,0.003334632,0.2804248,0.1865317,0.002631938],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03975996,0.001988363,0.9321848,0.02222155,0.0002600153,0.001417625,0.001069226,0.0003543431,0.0007441587],"genre_scores_gemma":[0.9878303,0.0001001375,0.01020449,0.0001459966,0.00004620812,0.0003452509,0.0000234232,0.00002715918,0.001276973],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9480704,"threshold_uncertainty_score":0.872201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04234432693086625,"score_gpt":0.3732053367692187,"score_spread":0.3308610098383525,"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."}}