{"id":"W4285451190","doi":"10.32920/ryerson.14646318.v1","title":"Capacity optimization for radio resource allocation in cognitive networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cognitive radio; Underlay; Computer science; Computer network; Quality of service; Overlay; Radio spectrum; Wireless; Spectrum management; Fading; Radio resource management; Wireless network; Channel (broadcasting); Telecommunications; Signal-to-noise ratio (imaging)","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"],"consensus_categories":[],"category_scores_codex":[0.0005847182,0.0002966349,0.0004184734,0.0001906414,0.0001207946,0.000557748,0.0004033096,0.0003572718,0.00001894138],"category_scores_gemma":[0.0001691904,0.0003283926,0.0001659986,0.0004566983,0.00004627404,0.0002537953,0.0005162849,0.0005829269,5.326476e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002149484,"about_ca_system_score_gemma":0.0001643454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001236456,"about_ca_topic_score_gemma":0.0003951251,"domain_scores_codex":[0.9977452,0.0002076548,0.0004292088,0.0009891458,0.00021434,0.0004145134],"domain_scores_gemma":[0.998394,0.0005023285,0.0002127573,0.0004453483,0.0003567459,0.00008887002],"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":[0.00001707596,0.00006261046,0.0001042824,0.0000304269,0.00004118657,0.00001222885,0.0005862794,0.9481973,0.000003496076,0.002654513,0.0002170813,0.04807351],"study_design_scores_gemma":[0.000499112,0.00002999431,0.0006124468,0.0004547753,0.00002301318,0.00001228019,0.0001028032,0.9970708,0.0001184162,0.0006238961,0.00007318058,0.000379276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005943886,0.0004695987,0.9887614,0.0008550306,0.0005424896,0.0009021595,0.000004144104,0.000155469,0.002365805],"genre_scores_gemma":[0.8290865,0.0001645615,0.1693595,0.0004981122,0.0003896157,0.00008157228,0.0002982761,0.00002580881,0.0000961145],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8231426,"threshold_uncertainty_score":0.9999168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03036873422748745,"score_gpt":0.2552283356179824,"score_spread":0.2248596013904949,"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."}}