{"id":"W2133435262","doi":"10.4236/cn.2013.53024","title":"Throughput Maximization Based on Optimal Access Probabilities in Cognitive Radio System","year":2013,"lang":"en","type":"article","venue":"Communications and Network","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Underlay; Cognitive radio; Computer science; Overlay; Throughput; Computer network; Scheme (mathematics); Markov process; Telecommunications; Signal-to-noise ratio (imaging); 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":[0.0002781811,0.0001388124,0.0001926199,0.00008840764,0.0003672962,0.0004386921,0.0007233331,0.0000587738,0.000008967544],"category_scores_gemma":[0.00002517648,0.0001306097,0.00003499203,0.0005867851,0.000137501,0.0005265828,0.0003980159,0.0002016259,0.000008193954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007499217,"about_ca_system_score_gemma":0.00004521937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009816104,"about_ca_topic_score_gemma":0.00007841677,"domain_scores_codex":[0.9988238,0.0002618477,0.0002483334,0.0002855654,0.0001125223,0.0002679769],"domain_scores_gemma":[0.998153,0.000719777,0.00009052397,0.0008512997,0.0001234348,0.00006194771],"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":[0.00007035134,0.000605184,0.0310821,0.0001338113,0.00008652351,0.0000184174,0.002727906,0.1094058,0.00001007139,0.3081543,0.002961195,0.5447443],"study_design_scores_gemma":[0.0003608331,0.00006665065,0.01386023,0.0003917593,0.000006324903,0.000007998108,0.0001448273,0.9833007,0.000005061334,0.001433152,0.0002620469,0.0001603808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04416133,0.002996955,0.9227766,0.004112363,0.0002434051,0.001498544,0.000004047332,0.0003148237,0.02389188],"genre_scores_gemma":[0.9707522,0.0003211864,0.02838887,0.0003062052,0.00007259075,0.0001082814,0.00001647145,0.0000101322,0.00002410573],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9265908,"threshold_uncertainty_score":0.5326111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03437426968491627,"score_gpt":0.2748567854655752,"score_spread":0.240482515780659,"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."}}