{"id":"W3132985895","doi":"10.1109/access.2021.3060863","title":"The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":568,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Big data; Data science; Artificial intelligence; Crystal twinning; Machine learning; Data mining","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":[0.0002077044,0.000103726,0.0002190247,0.0000469372,0.00002654122,0.000350773,0.0002476335,0.00005246942,0.000001696016],"category_scores_gemma":[0.0001070218,0.00007818612,0.00001304953,0.0001121581,0.00003545321,0.001163588,0.00009478984,0.0002491812,4.658884e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000700076,"about_ca_system_score_gemma":0.00001586712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001063797,"about_ca_topic_score_gemma":0.00001106486,"domain_scores_codex":[0.9993227,0.00002621906,0.0003073481,0.0001111461,0.0001254573,0.0001071031],"domain_scores_gemma":[0.9994608,0.0001272817,0.00004423893,0.0002790017,0.00005034481,0.0000383296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.00001847594,0.000102786,0.007318877,0.5181326,0.0004419712,0.0002629312,0.00513116,0.0004195822,0.0001013442,0.01073335,0.00163328,0.4557036],"study_design_scores_gemma":[0.002471277,0.0001493771,0.002196281,0.5422036,0.0003362297,0.001775773,0.0117294,0.04458254,0.003291705,0.008143242,0.3808976,0.002223005],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.01059264,0.9672087,0.00005300315,0.0004420922,0.0001777671,0.0002437581,0.000144682,0.00006273215,0.02107457],"genre_scores_gemma":[0.7785953,0.2211437,0.000003359309,0.00003464742,0.00002778605,0.00001624198,0.0000578988,0.0000136899,0.0001073461],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.7680026,"threshold_uncertainty_score":0.3382512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07287920984020752,"score_gpt":0.281595335328385,"score_spread":0.2087161254881775,"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."}}