{"id":"W2566309962","doi":"10.1002/dac.3247","title":"Resource allocation in heterogeneous cooperative cognitive radio networks","year":2016,"lang":"en","type":"article","venue":"International Journal of Communication Systems","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Cognitive radio; Cooperative diversity; Resource allocation; Relay; Computer network; Diversity (politics); Wireless; Channel (broadcasting); Resource (disambiguation); Interference (communication); Wireless network; Constraint (computer-aided design); Harm; Transmission (telecommunications); Telecommunications","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":[0.0009870583,0.000122145,0.0002320627,0.00030467,0.00006441738,0.0002304024,0.001252949,0.00006533528,0.000006823998],"category_scores_gemma":[0.0001870182,0.00009021473,0.00008358683,0.0002459471,0.00007057939,0.000686764,0.0001633276,0.000200084,0.000008209283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002767864,"about_ca_system_score_gemma":0.00008520755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002399205,"about_ca_topic_score_gemma":0.00003083006,"domain_scores_codex":[0.9978782,0.0006137264,0.0007296595,0.0001567215,0.0004638435,0.0001578671],"domain_scores_gemma":[0.9968788,0.000919999,0.0006478294,0.0003099886,0.001170777,0.00007261426],"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":[0.0008310142,0.0007338077,0.01100973,0.000005439786,0.001257393,0.0005562738,0.005950464,0.2772942,0.002443908,0.1583326,0.00296687,0.5386183],"study_design_scores_gemma":[0.01061721,0.0006923772,0.01837885,0.007497898,0.00005554493,0.004559237,0.001274751,0.9001686,0.002844078,0.002271679,0.05051773,0.001122028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04169194,0.003908414,0.9474455,0.004298497,0.0007720524,0.0001815748,0.000002899259,0.00002740853,0.001671752],"genre_scores_gemma":[0.9977845,0.001106281,0.000570686,0.0001656404,0.0002673648,0.000005643354,0.000003649045,0.00001004997,0.00008619083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9560925,"threshold_uncertainty_score":0.367885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01823933163246986,"score_gpt":0.2698964932296766,"score_spread":0.2516571615972067,"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."}}