{"id":"W4388550734","doi":"10.1016/j.aei.2023.102245","title":"Multiobjective optimization-based decision support for building digital twin maturity measurement","year":2023,"lang":"en","type":"article","venue":"Advanced Engineering Informatics","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Maturity (psychological); Standardization; Capability Maturity Model; Systems engineering; Engineering; Computer science; Process management; Risk analysis (engineering); Knowledge management; Business","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002099625,0.0002567442,0.0002067963,0.0003190117,0.00006884096,0.0001757105,0.0001868227,0.0001241449,0.000009438139],"category_scores_gemma":[0.0002228043,0.0002882238,0.00009756449,0.0005601468,0.00001449662,0.001694678,0.00002084916,0.0001849354,0.00005531705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002867245,"about_ca_system_score_gemma":0.00003752494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":1.360244e-7,"about_ca_topic_score_gemma":1.459791e-7,"domain_scores_codex":[0.9984373,0.000001418264,0.0006200192,0.00009342853,0.0004274815,0.0004203399],"domain_scores_gemma":[0.9992299,0.0001824615,0.00005417704,0.0002270819,0.0001895724,0.0001168242],"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":[0.000009086084,0.000007838791,0.0000109467,0.0003353906,0.00002686861,5.664759e-7,0.0002133265,0.9856457,0.0000657671,0.0001878217,0.0003585778,0.01313818],"study_design_scores_gemma":[0.0008081621,0.00003375872,0.00005427494,0.0001604264,0.000008999782,0.000002413857,0.0001524747,0.9823139,0.002775589,0.00005662381,0.01330796,0.0003254114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004275487,0.00000996236,0.9896562,0.000008242144,0.0007055069,0.0005108761,0.0001654769,0.001928511,0.002739747],"genre_scores_gemma":[0.653754,0.00001328559,0.345554,0.00002793862,0.00005230625,0.0002397762,0.0002458888,0.00009285184,0.00001992797],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6494785,"threshold_uncertainty_score":0.999957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0139164565316413,"score_gpt":0.2322927445359572,"score_spread":0.2183762880043159,"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."}}