{"id":"W4402157064","doi":"10.1109/icc51166.2024.10622422","title":"URLLC Latency Minimization in Interweave CRNs Using Digital Twin and DRL Approach","year":2024,"lang":"en","type":"article","venue":"","topic":"Wireless Body Area Networks","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Minification; Latency (audio); Telecommunications; World Wide Web","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":[0.00003997568,0.0001039386,0.00009675619,0.0001225343,0.00001140219,0.0001713523,0.00004527778,0.0000708252,0.00002098857],"category_scores_gemma":[0.000009362696,0.000099196,0.00001876153,0.0002260043,0.00001706745,0.0003542491,0.0000358238,0.0001211298,0.000007944662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005409837,"about_ca_system_score_gemma":0.000009087573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000109386,"about_ca_topic_score_gemma":0.00000933356,"domain_scores_codex":[0.9994819,0.000005785941,0.0001465971,0.0001517619,0.00005737784,0.0001566348],"domain_scores_gemma":[0.9998387,0.00002751005,0.000004766099,0.00007718118,0.000006651133,0.00004517254],"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":[0.00001055278,0.00006391446,0.01310963,0.0006138199,0.0001092582,0.0000899752,0.005039681,0.9052181,0.001113567,0.004092627,0.003848991,0.06668986],"study_design_scores_gemma":[0.00007606797,0.000004964365,0.0005164357,0.00008771066,0.000004760177,0.00001437707,0.000136726,0.9984648,0.0001038882,0.0001411191,0.0003282393,0.0001209028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7231283,0.0009956388,0.2263818,0.00003583301,0.0003585149,0.0001735501,0.000006612029,0.0006336226,0.04828619],"genre_scores_gemma":[0.9972389,0.00002495121,0.002387146,0.000008742502,0.00008051113,0.000004671688,0.00001700809,0.00003356589,0.0002045289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2741106,"threshold_uncertainty_score":0.4045096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0117251093069507,"score_gpt":0.2076552714362235,"score_spread":0.1959301621292728,"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."}}