{"id":"W4239273961","doi":"10.32920/14637045","title":"Throughput Maximization Based on Optimal Access Probabilities in Cognitive Radio System","year":2021,"lang":"en","type":"preprint","venue":"","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; Overlay; Computer science; Computer network; Throughput; Scheme (mathematics); Markov process; Maximization; Telecommunications; Signal-to-noise ratio (imaging); Mathematical optimization; Wireless; Mathematics","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004703437,0.0004509848,0.0006376827,0.0003386707,0.0001207658,0.001670323,0.0008418414,0.0002996464,0.00003917365],"category_scores_gemma":[0.0001153153,0.0004383022,0.0001970457,0.0006323181,0.00007422942,0.0005239929,0.001139305,0.00072806,0.000005087441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005706666,"about_ca_system_score_gemma":0.0005866558,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002619059,"about_ca_topic_score_gemma":0.0002165644,"domain_scores_codex":[0.9966614,0.0004138408,0.0005381409,0.001387687,0.0005166772,0.0004822977],"domain_scores_gemma":[0.9980781,0.000517803,0.0002268076,0.000725648,0.0003574379,0.00009420601],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001094683,0.0004244516,0.001897995,0.0009125127,0.0001104458,0.0009988489,0.002138074,0.9383293,0.000005489523,0.02437324,0.0001619552,0.03053822],"study_design_scores_gemma":[0.000601609,0.00006670598,0.002314048,0.002764717,0.000019939,0.00002591313,0.0004974658,0.9924264,0.0004190621,0.0003464241,0.000008884067,0.0005088478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03811516,0.0001784723,0.9387516,0.0005392413,0.001007081,0.001003402,0.00000967268,0.0003863758,0.02000893],"genre_scores_gemma":[0.969983,0.00002977089,0.02913375,0.0003550816,0.000208364,0.00008082982,0.00009179994,0.00002970487,0.00008765239],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9318679,"threshold_uncertainty_score":0.9998069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03493102983520968,"score_gpt":0.2790249010132846,"score_spread":0.2440938711780749,"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."}}