{"id":"W3094604992","doi":"10.1109/access.2020.3032204","title":"Fair Resource Allocation in Cooperative Cognitive Radio Iot Networks","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Sheikh Bahaei National High Performance Computing Center, Isfahan University of Technology; Isfahan University of Technology","keywords":"Underlay; Computer science; Cognitive radio; Relay; Computer network; Resource allocation; Throughput; Context (archaeology); Overlay; Spectral efficiency; Distributed computing; Wireless; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001936322,0.0001908437,0.000252443,0.00008249196,0.0001346342,0.0004395756,0.0006948567,0.00008354115,0.00001253708],"category_scores_gemma":[0.00008089436,0.0001910005,0.00005236437,0.00122924,0.00006345195,0.0006018928,0.0001617727,0.0003509944,0.00002012614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006252834,"about_ca_system_score_gemma":0.00006562771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005248259,"about_ca_topic_score_gemma":0.000119347,"domain_scores_codex":[0.9983597,0.0001965207,0.0002737906,0.0005824254,0.0002149241,0.0003725995],"domain_scores_gemma":[0.9991214,0.0003084073,0.00009787666,0.0001880229,0.0001296763,0.0001546385],"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":[0.0004995127,0.0003906889,0.01233664,0.00005957067,0.0002399691,0.001093895,0.01462311,0.418588,0.001524036,0.01580772,0.01894599,0.5158909],"study_design_scores_gemma":[0.0008553534,0.0001105015,0.009680266,0.0001091753,0.000009824344,0.00001352067,0.0001110563,0.9855419,0.001981882,0.0001826659,0.00106488,0.0003390188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09993535,0.0002989983,0.8920186,0.004322422,0.0002145671,0.0003496851,0.000001537828,0.0001617074,0.00269719],"genre_scores_gemma":[0.9937646,0.00003763468,0.0003565104,0.005265314,0.0005149808,0.00001268892,0.000007269653,0.00001634224,0.00002467638],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8938292,"threshold_uncertainty_score":0.7788773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03110261528376771,"score_gpt":0.2772383703523331,"score_spread":0.2461357550685654,"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."}}