{"id":"W2025119051","doi":"10.1109/tvt.2014.2322072","title":"Multi-Item Spectrum Auction for Recall-Based Cognitive Radio Networks With Multiple Heterogeneous Secondary Users","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cognitive radio; Computer science; Spectrum auction; Auction algorithm; Frequency allocation; Quality of service; Revenue; Auction theory; Payment; Revenue equivalence; Mathematical optimization; Computer network; Common value auction; Telecommunications; Microeconomics; Mathematics; Economics","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":[0.003832116,0.001190941,0.00225145,0.0006055096,0.001058706,0.002261133,0.003699768,0.001686444,0.001763459],"category_scores_gemma":[0.005644469,0.0006509887,0.001306995,0.0009870582,0.001491979,0.002673675,0.0016478,0.001442603,0.0002652098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00163172,"about_ca_system_score_gemma":0.001257376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002587802,"about_ca_topic_score_gemma":0.001777841,"domain_scores_codex":[0.9969149,0.001321463,0.0001364225,0.0005785422,0.0005525075,0.0004962388],"domain_scores_gemma":[0.9970312,0.001480714,0.0004620051,0.0003708504,0.0004075075,0.0002476256],"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":[0.0004114392,0.0002205386,0.00139002,0.0001687917,0.0002228679,0.0009455479,0.0002193541,0.9008459,0.00415652,0.06817182,0.001037408,0.02220984],"study_design_scores_gemma":[0.00004853318,0.0001082725,0.0001899731,0.000006022638,0.00003205352,0.0002393368,0.00003899122,0.9855242,0.0004369453,0.01282831,0.000525938,0.00002135129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06978412,0.0003921103,0.9257112,0.0001560325,0.00006833985,0.0001558426,0.00003746258,0.000118424,0.003576486],"genre_scores_gemma":[0.9492036,0.0001460081,0.04800407,0.00006708712,0.00005022361,0.0001001632,0.00005212631,0.00001944963,0.002357302],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003832116,"threshold_uncertainty_score":0.02026641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02790579824699583,"score_gpt":0.2906223305385253,"score_spread":0.2627165322915294,"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."}}