{"id":"W2003441926","doi":"10.1109/icumt.2012.6459785","title":"A price setting approach to power trading in cognitive radio networks","year":2012,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cistel Technology (Canada); Concordia University","funders":"Mitacs","keywords":"Cognitive radio; Computer science; Computer network; Profit (economics); Quality of service; Revenue; Transmitter power output; Radio spectrum; Renting; Key (lock); Bandwidth (computing); Telecommunications; Channel (broadcasting); Wireless; Computer security; Business; Transmitter; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002864508,0.001447193,0.001722654,0.0007098091,0.001191981,0.00338084,0.004033451,0.003574516,0.005870384],"category_scores_gemma":[0.008421292,0.001079567,0.00139568,0.001282057,0.002872782,0.004808258,0.00183081,0.00342868,0.0005890359],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002718564,"about_ca_system_score_gemma":0.001528224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002507143,"about_ca_topic_score_gemma":0.002514759,"domain_scores_codex":[0.9976138,0.001319891,0.00007827843,0.0003437709,0.0004375098,0.0002068087],"domain_scores_gemma":[0.9978605,0.001459425,0.0001790458,0.0001503014,0.0002191198,0.0001316578],"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":[0.00009008897,0.0001272852,0.0002876698,0.0001110435,0.00006314406,0.0002996866,0.0001571064,0.4437231,0.001264315,0.5300374,0.002354508,0.02148475],"study_design_scores_gemma":[0.00003177375,0.00005462901,0.00007097648,0.00001527353,0.00001934338,0.0001043638,0.00002311001,0.8046105,0.0001972129,0.1928782,0.001963225,0.00003147109],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01034014,0.001034237,0.9690529,0.001248126,0.0002679026,0.00008783815,0.00005256522,0.00009776893,0.01781856],"genre_scores_gemma":[0.8854831,0.001393354,0.09315275,0.0004021764,0.0005258425,0.0002270707,0.00003892086,0.00007704046,0.01869966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005870384,"threshold_uncertainty_score":0.01972467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01886130717433701,"score_gpt":0.246446765238106,"score_spread":0.227585458063769,"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."}}