{"id":"W4233701203","doi":"10.32920/ryerson.14662641","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; Interference (communication); Power (physics); Power control; Admission control; Function (biology); Toolbox; Linear programming; Wireless; Joint (building); Mathematics; Computer network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001505936,0.0008263699,0.0008400408,0.0004778081,0.000575889,0.001560117,0.001649861,0.001105149,0.001499825],"category_scores_gemma":[0.002758884,0.0003823737,0.0005531081,0.0007765768,0.0008967146,0.001121988,0.001360436,0.001230377,0.0001730029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00136179,"about_ca_system_score_gemma":0.001574103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004148318,"about_ca_topic_score_gemma":0.004592344,"domain_scores_codex":[0.9986118,0.0005033119,0.00004569579,0.0002094934,0.0002780667,0.0003515341],"domain_scores_gemma":[0.9988694,0.0006445221,0.0001827294,0.00005653733,0.0001178201,0.0001289925],"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":[0.0002619148,0.000209313,0.00123579,0.0001357638,0.00009172208,0.00029651,0.000150703,0.9039373,0.006582316,0.0273635,0.001606547,0.05812862],"study_design_scores_gemma":[0.00001260166,0.00005047083,0.0001671587,0.000004931275,0.00001790685,0.00003995085,0.00004025516,0.9930363,0.0008740936,0.005333757,0.0004147288,0.000007899914],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05248414,0.0006253043,0.941947,0.0004375967,0.0001033679,0.00008730747,0.00003486277,0.0001812012,0.004099237],"genre_scores_gemma":[0.9528597,0.0003246221,0.04354396,0.0001079937,0.00009020563,0.0001025093,0.0000377178,0.00002493432,0.002908373],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004148318,"threshold_uncertainty_score":0.009880483,"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."}}