{"id":"W4249088977","doi":"10.32920/ryerson.14662641.v1","title":"Joint admission control and power allocation in hospital networks based on cognitive radios.","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; Transmitter power output; Mathematical optimization; Computer science; Throughput; Power control; Interference (communication); Power (physics); Admission control; Function (biology); Toolbox; Joint (building); Linear programming; Wireless; Computer network; Mathematics; Transmitter; Telecommunications; Engineering","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":[0.0004552951,0.0003935225,0.000540208,0.0002362775,0.00009378202,0.0005445778,0.0002326558,0.0003332375,0.00003675554],"category_scores_gemma":[0.0001647522,0.0003706863,0.0001438735,0.0002824536,0.00005626389,0.0001760533,0.0003735411,0.000903608,0.000002398913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001491018,"about_ca_system_score_gemma":0.0002466551,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007412032,"about_ca_topic_score_gemma":0.00005966248,"domain_scores_codex":[0.9974123,0.0002896537,0.0004133353,0.001129589,0.0003371672,0.0004179851],"domain_scores_gemma":[0.9985358,0.000400231,0.0001916582,0.0004545376,0.0002154334,0.0002023611],"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":[0.0006710544,0.003720687,0.008469391,0.0003384317,0.0005807524,0.003414273,0.005287892,0.2993371,0.0003857604,0.01423986,0.002695331,0.6608595],"study_design_scores_gemma":[0.001543858,0.0002575766,0.02415805,0.001402041,0.00001973868,0.000009459734,0.00008424053,0.9716482,0.0001125341,0.0002695605,0.00002157773,0.0004731792],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05067666,0.0009123259,0.9404326,0.003051135,0.0008938985,0.0007419807,0.000003055265,0.000121887,0.003166442],"genre_scores_gemma":[0.9939893,0.0001801018,0.003830748,0.001729312,0.0001481307,0.00002385829,0.00004214699,0.00002352252,0.00003282342],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9433127,"threshold_uncertainty_score":0.9998745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008543225259179546,"score_gpt":0.2233912429114218,"score_spread":0.2148480176522423,"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."}}