{"id":"W4408282017","doi":"10.1109/iecon55916.2024.10905260","title":"Digital Twin for Industrial Asset Management: A Case Study for Pipeline Maintenance","year":2024,"lang":"en","type":"article","venue":"","topic":"Technology Assessment and Management","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Asset management; Pipeline (software); Asset (computer security); Computer science; Maintenance engineering; Business; Reliability engineering; Engineering; Computer security; Operating system; Finance","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":[0.0001474031,0.0001427597,0.0001277932,0.0001671434,0.00004093385,0.0001546489,0.0001216803,0.0000681022,0.00002088017],"category_scores_gemma":[0.000009550102,0.000125983,0.00006818277,0.0001680947,0.00001170245,0.0001917483,0.00006709772,0.00009316521,0.00001462919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005328023,"about_ca_system_score_gemma":0.000004382624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002956524,"about_ca_topic_score_gemma":0.00003037523,"domain_scores_codex":[0.9992593,0.000002347649,0.0001914747,0.0002268209,0.00006487883,0.0002551815],"domain_scores_gemma":[0.9997144,0.00005454682,0.000009110478,0.0001817427,0.00001375827,0.00002649541],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003013741,0.0001878089,0.0001861689,0.0003426695,0.0006359028,0.001630918,0.00009276473,0.0005173832,0.00000914168,0.03950873,0.5621468,0.3947116],"study_design_scores_gemma":[0.003127851,0.0003390296,0.00001550385,0.00004875443,0.0002239945,0.00008696308,0.005440266,0.1345493,0.00007743952,0.002132796,0.853581,0.0003771068],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0446482,0.00009192317,0.9254156,0.0004836798,0.001687924,0.004292896,0.0001509518,0.002879704,0.02034917],"genre_scores_gemma":[0.9799027,0.000005045274,0.002471353,0.0000219052,0.0001485587,0.0009522366,0.00003485842,0.00003890194,0.01642446],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9352545,"threshold_uncertainty_score":0.5137437,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02639219889894694,"score_gpt":0.2777259097514606,"score_spread":0.2513337108525137,"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."}}