{"id":"W2126666292","doi":"10.1109/spawc.2009.5161753","title":"Truth reveling opportunistic scheduling in cognitive radio systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Nash equilibrium; Cognitive radio; Scheduling (production processes); Mechanism design; Mathematical optimization; Distributed computing; Mathematical economics","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":[0.00410086,0.0004106543,0.0005628481,0.0005202043,0.0006373018,0.001299917,0.001106239,0.00106076,0.001063102],"category_scores_gemma":[0.01025921,0.0003540006,0.0003751363,0.0006390269,0.001925434,0.001867722,0.0009268907,0.0007866331,0.0001048121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001153929,"about_ca_system_score_gemma":0.001445187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002058354,"about_ca_topic_score_gemma":0.001391497,"domain_scores_codex":[0.9978943,0.001240722,0.00007775581,0.0002081506,0.0003546595,0.000224441],"domain_scores_gemma":[0.993775,0.004424684,0.0008239889,0.0004109884,0.0003542131,0.0002111862],"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":[0.0003428776,0.000126316,0.001240034,0.0001297114,0.00006750395,0.0004870474,0.0003964827,0.6406354,0.003835397,0.3183034,0.001056033,0.03337982],"study_design_scores_gemma":[0.0000600624,0.00005719761,0.0001559517,0.000007007578,0.000009600227,0.00008723207,0.00003946275,0.8950946,0.0005471584,0.1033596,0.0005657921,0.00001621907],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1820059,0.000696477,0.806703,0.001073933,0.00008819634,0.0001149134,0.00005323678,0.0002115018,0.009052704],"genre_scores_gemma":[0.9817954,0.0001192591,0.017152,0.0000558303,0.00002152411,0.00003351853,0.000007897463,0.000006954714,0.0008075893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00410086,"threshold_uncertainty_score":0.02168769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03475238206691344,"score_gpt":0.2695802056502508,"score_spread":0.2348278235833373,"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."}}