{"id":"W3022804041","doi":"","title":"Demand-Matching Spectrum Sharing in Cognitive Radio Networks: A Classified Game","year":2012,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Cognitive radio; Computer science; Regret; Channel (broadcasting); Wireless; Computer network; Spectrum management; Radio spectrum; Matching (statistics); Game theory; Distributed computing; Telecommunications; Machine learning; Mathematics","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":[0.0008273076,0.0002683256,0.0003432478,0.0002314399,0.0001378993,0.0002937597,0.0004580182,0.0001042176,0.0000707092],"category_scores_gemma":[0.00004025985,0.0002522896,0.0001097312,0.0007923365,0.00004762169,0.001224461,0.0003635529,0.0004416482,0.0000419185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001351443,"about_ca_system_score_gemma":0.00002973559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001135473,"about_ca_topic_score_gemma":0.0002551094,"domain_scores_codex":[0.997589,0.0001068837,0.0003798588,0.000551676,0.0002509287,0.001121673],"domain_scores_gemma":[0.998924,0.0003638796,0.0001034076,0.000331793,0.00003200688,0.0002448688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001967196,0.001110102,0.2543965,0.0000530955,0.0003439938,0.0005894023,0.0184744,0.01616455,0.0005552419,0.5325038,0.0009699572,0.1746422],"study_design_scores_gemma":[0.001367927,0.00006302255,0.1164462,0.000290729,0.00001994194,0.0001799805,0.0003535249,0.8710861,0.0003860416,0.008856934,0.0002451848,0.000704408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1825211,0.0008225525,0.7962113,0.0003846558,0.000530758,0.0002188514,3.581616e-7,0.0002298962,0.01908054],"genre_scores_gemma":[0.9952051,0.00007971847,0.003123757,0.0005987039,0.0006798227,0.00000905307,0.000003037496,0.00002284853,0.0002779699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8549216,"threshold_uncertainty_score":0.9999929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02541965210214913,"score_gpt":0.2600741744318591,"score_spread":0.23465452232971,"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."}}