{"id":"W1599162309","doi":"10.1109/infocom.2015.7218410","title":"Priority differentiation in cognitive radio networks","year":2015,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cognitive radio; Computer science; Differentiated services; Scheduling (production processes); Prioritization; Computer network; Probabilistic logic; Network packet; Duration (music); Cognitive network; Channel (broadcasting); Distributed computing; Telecommunications; Wireless; Artificial intelligence; Engineering; Process management","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":[0.0002972227,0.0001043783,0.0001436329,0.00008932181,0.00004060567,0.0001307572,0.000171072,0.0000550595,0.000009910696],"category_scores_gemma":[0.00005835209,0.00009510363,0.00003426575,0.0004334766,0.0000222125,0.0003677788,0.0001023269,0.0001596481,0.00001202113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008308385,"about_ca_system_score_gemma":0.0000483675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007491536,"about_ca_topic_score_gemma":0.0002940152,"domain_scores_codex":[0.9990104,0.00009378276,0.0001640101,0.0002872568,0.0001753418,0.000269185],"domain_scores_gemma":[0.9994722,0.0001202566,0.00004439083,0.0001485165,0.00009506263,0.0001196004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009542263,0.0004372766,0.1277443,0.000006769336,0.00006317036,0.0002311006,0.003648604,0.005851896,0.00004758964,0.1450516,0.003441876,0.7133803],"study_design_scores_gemma":[0.0008850247,0.00005447957,0.0873573,0.00002961575,0.000004032391,0.00001623242,0.00006619427,0.9070726,0.00007083806,0.004164542,0.0001051282,0.0001739994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09350962,0.0001736672,0.893233,0.0002611035,0.0003599565,0.0001322747,1.993447e-7,0.0001086835,0.0122215],"genre_scores_gemma":[0.9951994,0.00001979044,0.004260881,0.0001994275,0.0001570247,0.000002718086,0.000003732373,0.000005017302,0.0001520139],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9016898,"threshold_uncertainty_score":0.3878213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02643787971602242,"score_gpt":0.2576398047920216,"score_spread":0.2312019250759992,"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."}}