{"id":"W3033971127","doi":"10.1109/access.2020.2998723","title":"Digital Twin for the Oil and Gas Industry: Overview, Research Trends, Opportunities, and Challenges","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":357,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Memorial University of Newfoundland","funders":"Memorial University of Newfoundland; Atlantic Canada Opportunities Agency; Mitacs; University of Toronto; Petroleum Research Newfoundland and Labrador","keywords":"Fossil fuel; Petroleum industry; Computer science; Data science; Environmental science; Engineering; Waste management","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001653408,0.0001177661,0.0001189332,0.00007597071,0.00009174948,0.0006035876,0.0002734393,0.0001499495,0.00002692291],"category_scores_gemma":[0.0000363959,0.00009497719,0.0000209991,0.0001836847,0.0001090867,0.001232755,0.00005599512,0.0003906215,0.000003078581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001437004,"about_ca_system_score_gemma":0.00001907158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003466254,"about_ca_topic_score_gemma":0.000004375092,"domain_scores_codex":[0.9992525,0.00001258123,0.0001614151,0.0001397074,0.0001969692,0.0002368565],"domain_scores_gemma":[0.9993883,0.0002706473,0.00001497576,0.0001319048,0.00004605493,0.0001480961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001039413,0.000007150997,0.00006313764,0.0004044964,0.00004064977,0.000003696858,0.0006449784,0.0002307785,0.000006735195,0.001670319,0.01574977,0.9811679],"study_design_scores_gemma":[0.000482899,0.00005804926,0.0004808591,0.00007897096,0.00001241351,0.00001191349,0.002533083,0.0043945,0.0003646521,0.0004175572,0.9909478,0.000217326],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1266693,0.03420078,0.0004214978,0.1016403,0.0009090565,0.0004324512,0.001039535,0.0007671568,0.7339199],"genre_scores_gemma":[0.9776345,0.02055364,0.00001208094,0.0002233213,0.0003712351,0.0001133805,0.00001712413,0.00004376913,0.001030894],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9809506,"threshold_uncertainty_score":0.5820409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4065423539478378,"score_gpt":0.3655984489252738,"score_spread":0.04094390502256401,"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."}}