{"id":"W4407831631","doi":"10.1109/mcomstd.0001.2300054","title":"Toward 6G-Enabled URLLCs: Digital Twin, Open Ran, and Semantic Communications","year":2025,"lang":"en","type":"article","venue":"IEEE Communications Standards Magazine","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Ran; Telecommunications; Computer network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002618229,0.0005398258,0.000325295,0.001003933,0.0009603528,0.004566898,0.0009700726,0.001976732,0.002293245],"category_scores_gemma":[0.001896868,0.0002960762,0.0003630309,0.00116556,0.00243709,0.009270229,0.003465035,0.003533181,0.001135742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001014265,"about_ca_system_score_gemma":0.001790271,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001138855,"about_ca_topic_score_gemma":0.001564787,"domain_scores_codex":[0.9987697,0.0003528385,0.00006371808,0.0001387715,0.0005043961,0.0001706497],"domain_scores_gemma":[0.9989506,0.00025913,0.000110767,0.0001777405,0.0003540599,0.0001477563],"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":[0.00005091033,0.00002991939,0.0003139962,0.0001312501,0.000007504964,0.000155953,0.000493245,0.00190087,0.005716812,0.9126142,0.003498814,0.07508653],"study_design_scores_gemma":[0.00001433209,0.0002052177,0.0003944846,0.0003650367,0.0000290702,0.0009272798,0.001343016,0.04357741,0.01019679,0.5513432,0.391539,0.00006522165],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02164195,0.008424923,0.8542333,0.008893397,0.001280615,0.0001011624,0.00008175618,0.001028173,0.1043147],"genre_scores_gemma":[0.4552355,0.01813773,0.487807,0.003893819,0.001552862,0.0001814032,0.000340784,0.0002945022,0.03255643],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004566898,"threshold_uncertainty_score":0.01384664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03998603399522638,"score_gpt":0.3098374726713112,"score_spread":0.2698514386760848,"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."}}