{"id":"W4402753579","doi":"10.1109/cis-ram61939.2024.10672933","title":"Advancing Offshore Operations with Digital Twin Technology: A Case Study of FLNG Operations in Offshore Environments","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":"University of Toronto","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China; National Research Foundation","keywords":"Submarine pipeline; Marine engineering; Computer science; Petroleum engineering; Engineering; Geotechnical engineering","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":[0.00005426148,0.0001465898,0.0001556347,0.0005263785,0.00005027202,0.00005868557,0.0001147276,0.00007495008,0.00003804214],"category_scores_gemma":[0.00000468802,0.0001248905,0.0000164296,0.0005650338,0.00004373354,0.0003987824,0.00008750406,0.0002085614,0.00001092285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008865784,"about_ca_system_score_gemma":0.00001442456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004217622,"about_ca_topic_score_gemma":0.005414775,"domain_scores_codex":[0.9992513,0.000005636174,0.0002411506,0.0002176387,0.00009975759,0.000184494],"domain_scores_gemma":[0.9996929,0.0000106067,0.000004998262,0.0002622182,0.000006838818,0.00002239603],"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":[0.000008088798,0.001352249,0.05888614,0.0001592547,0.0006885256,0.006468911,0.004941763,0.8749052,0.003868244,0.01499333,0.0006202747,0.03310798],"study_design_scores_gemma":[0.002897752,0.00164352,0.004592849,0.0003495113,0.0002746154,0.0009855767,0.1982836,0.7770094,0.006109469,0.0002464118,0.006408274,0.001199007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9758267,0.0001397254,0.02145266,0.0001948423,0.0000624179,0.0006023945,0.000007499833,0.0005329988,0.001180764],"genre_scores_gemma":[0.9971949,0.00001468507,0.002184815,0.000005406123,0.000007388982,0.0001924874,0.00001040789,0.00002650955,0.0003633415],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1933418,"threshold_uncertainty_score":0.5092887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005340693827848857,"score_gpt":0.2329281683476419,"score_spread":0.227587474519793,"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."}}