{"id":"W2017147526","doi":"10.1109/wimob.2010.5644851","title":"Network assisted auctioning for cognitive radios","year":2010,"lang":"en","type":"article","venue":"","topic":"Cognitive Radio Networks and Spectrum Sensing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cognitive radio; Computer network; Computer science; Node (physics); Flexibility (engineering); Wireless; Channel (broadcasting); Revenue; Interference (communication); White spaces; Cognitive network; Wireless network; Spectrum management; Telecommunications; Business; 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":[0.002164121,0.000547384,0.0008243996,0.000483583,0.0006507948,0.001780515,0.001779335,0.0007962852,0.003228583],"category_scores_gemma":[0.003669826,0.0003644273,0.0006111842,0.0005196171,0.001047173,0.00190056,0.001061003,0.001051713,0.0003960897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001267825,"about_ca_system_score_gemma":0.001309875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00167685,"about_ca_topic_score_gemma":0.001473173,"domain_scores_codex":[0.9984132,0.0007001535,0.00005464595,0.0001684569,0.0004224297,0.0002411554],"domain_scores_gemma":[0.9981517,0.000993398,0.0001769322,0.0002120459,0.0003177711,0.0001480457],"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":[0.0005834152,0.0004222747,0.0008487969,0.0002211643,0.0001388288,0.0005091643,0.0002371541,0.6142295,0.0102357,0.2814475,0.003022505,0.08810395],"study_design_scores_gemma":[0.00004656618,0.0000833068,0.00009106145,0.000006382911,0.00001374576,0.00009668172,0.00002206032,0.963911,0.001180766,0.03285724,0.001675842,0.00001546415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04965665,0.0003283539,0.9344907,0.0001972885,0.0001133869,0.0001384296,0.00002693998,0.0002796703,0.01476851],"genre_scores_gemma":[0.9261471,0.0001525965,0.06619862,0.00005852187,0.00004296317,0.0001146196,0.00002349312,0.00003182926,0.007230185],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003228583,"threshold_uncertainty_score":0.01144511,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01714598233351042,"score_gpt":0.2525824095702437,"score_spread":0.2354364272367333,"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."}}