{"id":"W4394744622","doi":"10.1109/jrfid.2024.3387996","title":"Digital Twin Models: Functions, Challenges, and Industry Applications","year":2024,"lang":"en","type":"article","venue":"IEEE Journal of Radio Frequency Identification","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":52,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Research Council Canada","keywords":"Computer science; Industrial engineering; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002570247,0.000137129,0.0001433169,0.000349448,0.00004835882,0.0004190154,0.0001668402,0.0002216515,0.00001487826],"category_scores_gemma":[0.00001258649,0.000140069,0.00007506099,0.0002504289,0.00005711833,0.003466984,0.000003156954,0.0006152531,0.00005986786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001570381,"about_ca_system_score_gemma":0.00005926861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":5.89396e-7,"about_ca_topic_score_gemma":6.802793e-7,"domain_scores_codex":[0.9988033,0.0000129353,0.0006504669,0.0001312753,0.0002644265,0.0001376191],"domain_scores_gemma":[0.9994224,0.00005708537,0.00008853834,0.0001832341,0.0001320752,0.0001166958],"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":[0.000007689376,0.0001140701,0.0001069454,0.00105983,0.0004912553,0.00003330078,0.001518311,0.03808454,0.009092782,0.0415941,0.01355251,0.8943447],"study_design_scores_gemma":[0.002515254,0.0004194105,0.006407449,0.003526575,0.001052042,0.01027625,0.009231159,0.1206876,0.01784945,0.3226355,0.5019748,0.003424455],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09130677,0.1446871,0.5512821,0.001708742,0.00754691,0.0009012844,0.000343431,0.001133716,0.2010899],"genre_scores_gemma":[0.9956421,0.002944791,0.0001890894,0.000005252882,0.0005182335,0.00003525002,0.00001491938,0.00003511555,0.0006152672],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9043353,"threshold_uncertainty_score":0.571185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03226270485462468,"score_gpt":0.2370151127791138,"score_spread":0.2047524079244891,"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."}}