{"id":"W2739331082","doi":"10.1155/2017/7385627","title":"Resource Allocation in Heterogeneous Buffered Cognitive Radio Networks","year":2017,"lang":"en","type":"article","venue":"Wireless Communications and Mobile Computing","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Queueing theory; Queue; Computer network; Resource allocation; Throughput; Blocking (statistics); Cognitive radio; Heterogeneous network; Service (business); Base station; Resource (disambiguation); Network performance; Distributed computing; Telecommunications; Wireless 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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0004744933,0.0001752654,0.0002577535,0.0001022667,0.001503167,0.0006363992,0.001426992,0.00008450357,0.000001160817],"category_scores_gemma":[0.00004098511,0.000190772,0.00005271154,0.0001564777,0.0002522534,0.0002790889,0.001521647,0.0003409834,0.00000193237],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000052878,"about_ca_system_score_gemma":0.00003127161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001487673,"about_ca_topic_score_gemma":0.0002344706,"domain_scores_codex":[0.9985819,0.0002271237,0.000327518,0.0004091027,0.0001149408,0.0003393941],"domain_scores_gemma":[0.9972666,0.0005119195,0.0002682124,0.001759638,0.0001006861,0.00009293166],"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":[0.000008126653,0.0001003365,0.004078329,0.000007825659,0.0000261361,0.00001423301,0.001360899,0.003502621,0.00008080966,0.008392544,0.00001672626,0.9824114],"study_design_scores_gemma":[0.000521721,0.00005231201,0.01409671,0.000260191,0.000008120415,0.00005493502,0.0001411972,0.9834579,0.00006113353,0.0001860241,0.0009330171,0.0002267966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5425174,0.003328935,0.4506343,0.0007311109,0.0001161576,0.0004587943,0.000001539513,0.0001280356,0.002083691],"genre_scores_gemma":[0.9947025,0.001017307,0.004007055,0.0001209085,0.00007824064,0.00002355172,0.0000147023,0.00001592111,0.00001980363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9821846,"threshold_uncertainty_score":0.9997967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02195629551575975,"score_gpt":0.2753662703334591,"score_spread":0.2534099748176993,"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."}}