{"id":"W2325372342","doi":"10.1049/iet-com.2015.0906","title":"Performance study of opportunistic scheduling in dual‐hop multi‐user underlay cognitive network","year":2016,"lang":"en","type":"article","venue":"IET Communications","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Cumulative distribution function; Scheduling (production processes); Cognitive radio; Probability density function; Mathematical optimization; Monte Carlo method; Diversity gain; Fading; Algorithm; Mathematics; Telecommunications; Statistics; Wireless","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":[0.0007702946,0.0001485245,0.0002337691,0.0001390065,0.0003747161,0.00004686602,0.001856085,0.0000488156,0.00002047348],"category_scores_gemma":[0.0001905136,0.0001225018,0.00004021028,0.0008739122,0.0001704303,0.0004397799,0.001774241,0.0002425662,0.00003232269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006393393,"about_ca_system_score_gemma":0.0001197259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001726209,"about_ca_topic_score_gemma":0.0008640246,"domain_scores_codex":[0.9981636,0.0006269374,0.0005165526,0.0002583061,0.0001759548,0.0002586212],"domain_scores_gemma":[0.9956639,0.001132836,0.0002126667,0.002603565,0.0003119673,0.00007510787],"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":[0.0000828783,0.008698802,0.3071729,0.00005007197,0.0003669568,0.00001704666,0.04043394,0.005276073,0.003794591,0.227401,0.0005686931,0.406137],"study_design_scores_gemma":[0.00838865,0.000822208,0.3685128,0.002048137,0.00007655595,0.0000231339,0.005535412,0.6071309,0.0002135885,0.0007444831,0.005305962,0.001198201],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7648801,0.001055487,0.2248459,0.00353785,0.0001951287,0.001071021,0.000007854554,0.0002138252,0.00419292],"genre_scores_gemma":[0.9789223,0.002612216,0.01792016,0.0001363605,0.0000178361,0.00009211772,0.000005593326,0.00001225076,0.0002811754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6018548,"threshold_uncertainty_score":0.4995477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1385949911132258,"score_gpt":0.3460130080551114,"score_spread":0.2074180169418856,"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."}}