{"id":"W2131241853","doi":"10.1109/isic.2007.4450938","title":"Toward A Practical Multi-agent System for Integrated Control and Asset Management of Petroleum Production Facilities","year":2007,"lang":"en","type":"article","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Fredericton; University of New Brunswick","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Cape Breton University","keywords":"Computer science; Asset management; Asset (computer security); Systems engineering; Component (thermodynamics); Plan (archaeology); Intelligent agent; Petroleum industry; Engineering management; Software engineering; Engineering; Artificial intelligence; Computer security","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":[0.0008404484,0.0004278716,0.000401375,0.000255005,0.0006156165,0.001131111,0.001223413,0.001261454,0.00186405],"category_scores_gemma":[0.001301676,0.0002382355,0.0002340789,0.0002252984,0.0005921233,0.0009659211,0.001298954,0.0008466214,0.0004783069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004461933,"about_ca_system_score_gemma":0.001200232,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001413846,"about_ca_topic_score_gemma":0.001819539,"domain_scores_codex":[0.9995468,0.0001465914,0.00003109815,0.0000813858,0.0001648524,0.00002916374],"domain_scores_gemma":[0.9996954,0.00007699378,0.00003971419,0.00003792925,0.0001050945,0.00004485443],"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":[0.0002347124,0.000300065,0.001566336,0.0003296141,0.0000746626,0.0007713728,0.0007303442,0.6255792,0.04762951,0.09568136,0.004600518,0.2225023],"study_design_scores_gemma":[0.00005081088,0.0001210746,0.000121072,0.00001489115,0.00001647941,0.0000916065,0.00004639665,0.9819223,0.003144331,0.005642766,0.008819555,0.000008743342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008356168,0.0001104378,0.9876025,0.0002805727,0.00003584597,0.0001320939,0.00001101375,0.0004609594,0.003010242],"genre_scores_gemma":[0.3160702,0.0002187934,0.6801047,0.000124279,0.00006348547,0.00029618,0.00006506087,0.00003453528,0.003022775],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00186405,"threshold_uncertainty_score":0.006235838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02316908682327136,"score_gpt":0.2563984853882791,"score_spread":0.2332293985650077,"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."}}