{"id":"W2119062559","doi":"10.1109/infcom.2010.5462169","title":"Efficient Resource Allocation with Flexible Channel Cooperation in OFDMA Cognitive Radio Networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Cognitive radio; Channel allocation schemes; Resource allocation; Computer network; Channel (broadcasting); Selfishness; Heuristics; Leverage (statistics); Relay; Distributed computing; Telecommunications; Wireless","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":[0.0004709383,0.0001319695,0.0001262788,0.0001333884,0.0002088425,0.0001687264,0.0004899664,0.0000672014,0.00005269695],"category_scores_gemma":[0.00004142732,0.0001080423,0.00001940725,0.0009038516,0.00006147775,0.0001718891,0.0001711479,0.0003520189,0.0000174417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002920375,"about_ca_system_score_gemma":0.00006603223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001458513,"about_ca_topic_score_gemma":0.0003784361,"domain_scores_codex":[0.9989607,0.000126828,0.0002026154,0.00032453,0.0001575881,0.0002277243],"domain_scores_gemma":[0.999046,0.00014864,0.00006541156,0.0004355069,0.0002292706,0.00007516834],"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":[0.0001304588,0.000686803,0.002025596,0.00001328257,0.00004418421,0.000009165034,0.0057342,0.5099508,0.00663446,0.3337542,0.001691344,0.1393255],"study_design_scores_gemma":[0.0006068113,0.00005769312,0.003929318,0.00005263952,0.000002994688,0.000006981305,0.0000607778,0.9918088,0.002206576,0.00001426681,0.001077584,0.0001755436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1051392,0.0001554416,0.8850918,0.001975155,0.0001337999,0.0003668797,3.538335e-7,0.000183941,0.006953408],"genre_scores_gemma":[0.9953688,0.00007560752,0.003155219,0.0005720569,0.00006731284,0.00008082283,0.00001232393,0.000009525767,0.0006582998],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8902296,"threshold_uncertainty_score":0.4405838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01999728062831468,"score_gpt":0.2534772495305175,"score_spread":0.2334799689022029,"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."}}