{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001140474,0.0005355651,0.0004329085,0.0009127986,0.0003067167,0.0006113417,0.0008593484,0.0005549766,0.001248835],"category_scores_gemma":[0.002803077,0.0003523835,0.000476169,0.0004042365,0.0002835704,0.0006876756,0.0005750654,0.0004834586,0.0001000039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001466806,"about_ca_system_score_gemma":0.0009315521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004768017,"about_ca_topic_score_gemma":0.005245197,"domain_scores_codex":[0.9994723,0.0001814277,0.00002012544,0.00007796496,0.0001943001,0.00005390185],"domain_scores_gemma":[0.9992618,0.0004124697,0.0001503346,0.00003559207,0.0001097493,0.00003007208],"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":[0.00005334875,0.0000419577,0.001259221,0.00003911473,0.00001924275,0.00005402107,0.00002884393,0.963379,0.001459004,0.00393245,0.0002396072,0.02949424],"study_design_scores_gemma":[0.000005858072,0.00004529853,0.0007682203,0.000006065415,0.00001194147,0.00002983967,0.00001341137,0.9959698,0.0004863903,0.002380437,0.0002773131,0.000005488102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1284889,0.000461485,0.8658042,0.0002609567,0.0000163791,0.0001572866,0.0001225173,0.0002146786,0.004473715],"genre_scores_gemma":[0.949599,0.0001573653,0.04909808,0.00001275217,0.000006586488,0.00008638209,0.00008408423,0.00001781546,0.0009379273],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004768017,"threshold_uncertainty_score":0.01064253,"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."}}