{"id":"W4407351697","doi":"10.1109/ojcoms.2025.3540287","title":"The Role of Digital Twin in 6G-Based URLLCs: Current Contributions, Research Challenges, and Next Directions","year":2025,"lang":"en","type":"article","venue":"IEEE Open Journal of the Communications Society","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; Engineering and Physical Sciences Research Council; Canada Excellence Research Chairs, Government of Canada","keywords":"Current (fluid); Twin study; Data science; Computer science; Engineering; Electrical engineering; Biology; Evolutionary biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00131287,0.0007020476,0.0006499459,0.0007851357,0.000606293,0.002946631,0.001484598,0.001707653,0.005728264],"category_scores_gemma":[0.002285748,0.0002243034,0.0003281572,0.001038969,0.001987153,0.005875078,0.002467049,0.002206031,0.0012397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008819763,"about_ca_system_score_gemma":0.001173003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001122272,"about_ca_topic_score_gemma":0.001491884,"domain_scores_codex":[0.999524,0.0001211669,0.00002040206,0.00009538268,0.0001551581,0.0000839238],"domain_scores_gemma":[0.9985719,0.0006776098,0.0001083299,0.0001491753,0.0003406459,0.0001523212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001111656,0.00008906955,0.001821011,0.001023305,0.00003677365,0.0003173163,0.0003232535,0.01994374,0.004806555,0.4957052,0.0108905,0.4649321],"study_design_scores_gemma":[0.0000217511,0.0004112567,0.0008961585,0.001112899,0.00009890495,0.001223001,0.001416972,0.1942447,0.007077916,0.4022792,0.3911073,0.0001098915],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.04299415,0.254853,0.5166199,0.02848798,0.003440245,0.0001929716,0.0002922639,0.0008781804,0.1522412],"genre_scores_gemma":[0.7189399,0.1629881,0.09460224,0.004007499,0.002466331,0.0001472127,0.0002985028,0.0001610193,0.01638923],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005728264,"threshold_uncertainty_score":0.01916301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1018526547825813,"score_gpt":0.3736816066678986,"score_spread":0.2718289518853173,"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."}}