{"id":"W840214300","doi":"10.1016/j.sigpro.2015.04.010","title":"On imperfect pricing in globally constrained noncooperative games for cognitive radio networks","year":2015,"lang":"en","type":"article","venue":"Signal Processing","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; National Key Research and Development Program of China; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Imperfect; Cognitive radio; Nash equilibrium; Mathematical optimization; Mathematical economics; Perfect information; Computer science; Monotone polygon; Class (philosophy); Contrast (vision); Economics; Mathematics; Telecommunications; Artificial intelligence","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":[0.0008220277,0.000273554,0.0003617492,0.000154478,0.0002159021,0.0004695483,0.000298563,0.000100177,0.000003506882],"category_scores_gemma":[0.0001916422,0.0002478936,0.0000763366,0.000675036,0.0001168186,0.0005652694,0.00008016956,0.0002962936,0.000003696267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002121346,"about_ca_system_score_gemma":0.0004908466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001560751,"about_ca_topic_score_gemma":0.0000390838,"domain_scores_codex":[0.9980182,0.0001348952,0.0003336981,0.0006117912,0.000302593,0.0005988265],"domain_scores_gemma":[0.9985669,0.0006255937,0.0001480106,0.0001124267,0.000366925,0.0001801149],"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":[0.0004673972,0.000175076,0.0004388663,0.00004259375,0.00005337477,0.0001718922,0.004107149,0.04708328,0.0003352169,0.01170466,0.0004695844,0.9349509],"study_design_scores_gemma":[0.002269741,0.0006235392,0.0002144006,0.000771161,0.00001523533,0.00005896256,0.0004473001,0.9901031,0.0006382978,0.004425006,0.00005049301,0.0003827495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02982965,0.0009157415,0.9607938,0.0002629218,0.0001544701,0.0005443742,0.000003003411,0.0001280113,0.007368051],"genre_scores_gemma":[0.9911594,0.000006446453,0.007728133,0.0007260937,0.0002958686,0.00002362692,0.000006877296,0.00002109202,0.0000324416],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9613298,"threshold_uncertainty_score":0.9999973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01972069609413558,"score_gpt":0.2696917719697017,"score_spread":0.2499710758755662,"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."}}