{"id":"W1435481067","doi":"10.1007/978-3-642-30493-4_51","title":"Demand-Matching Spectrum Sharing in Cognitive Radio Networks: A Classified Game","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Cognitive radio; Computer science; Regret; Wireless; Channel (broadcasting); Computer network; Spectrum management; Radio spectrum; Matching (statistics); Spectrum (functional analysis); Wireless network; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001602662,0.001045431,0.001175756,0.0004806311,0.0008408336,0.003021363,0.002571037,0.003076424,0.005535348],"category_scores_gemma":[0.003776271,0.0004310945,0.0008534912,0.0009535154,0.001818205,0.003271251,0.001950737,0.001967368,0.0004811654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002711304,"about_ca_system_score_gemma":0.001403934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002001334,"about_ca_topic_score_gemma":0.001791495,"domain_scores_codex":[0.9986841,0.0006678949,0.00004213212,0.0001457529,0.0002569132,0.0002031684],"domain_scores_gemma":[0.9984096,0.001090721,0.00008936464,0.00007945201,0.000120318,0.0002106005],"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":[0.0003778926,0.0002792533,0.0004586349,0.0002024738,0.00007570771,0.000225901,0.0002760785,0.2118378,0.002915454,0.7511312,0.00819698,0.02402263],"study_design_scores_gemma":[0.0001003726,0.00008660993,0.0001341413,0.00003262028,0.00001972702,0.0001207031,0.0001127067,0.6203728,0.0003630847,0.3750552,0.003577583,0.00002450099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1148801,0.0009942689,0.7657408,0.002788696,0.0002202919,0.0004962925,0.0003169569,0.0001442596,0.1144183],"genre_scores_gemma":[0.8992642,0.001010477,0.06725291,0.0004233698,0.0002132486,0.0003891306,0.0001511829,0.00006680163,0.03122877],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005535348,"threshold_uncertainty_score":0.01967204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02676646299219956,"score_gpt":0.2447074077831201,"score_spread":0.2179409447909206,"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."}}