{"id":"W2045457657","doi":"10.1109/icc.2014.6883555","title":"Combinatorial spectrum auction with multiple heterogeneous sellers in cognitive radio networks","year":2014,"lang":"en","type":"article","venue":"","topic":"Auction Theory and Applications","field":"Decision Sciences","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Cognitive radio; Knapsack problem; Combinatorial auction; Computer science; Spectrum auction; Mathematical optimization; Spectrum (functional analysis); Auction algorithm; Incentive compatibility; Frequency allocation; Greedy algorithm; Channel (broadcasting); Payment; Mechanism design; Incentive; Auction theory; Common value auction; Computer network; Mathematics; Algorithm; Mathematical economics; Revenue equivalence; Wireless; Microeconomics; Telecommunications; Economics","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":[0.003887875,0.0007685617,0.001793785,0.0006864334,0.000915578,0.002505539,0.002162952,0.001434193,0.001085202],"category_scores_gemma":[0.005604437,0.0006828406,0.0009503251,0.001291043,0.001537077,0.002670267,0.001317537,0.001160475,0.0001533907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001324006,"about_ca_system_score_gemma":0.001123675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002388295,"about_ca_topic_score_gemma":0.00161572,"domain_scores_codex":[0.9968599,0.001971304,0.0001041478,0.0003072108,0.0004583867,0.0002990906],"domain_scores_gemma":[0.9977539,0.001549921,0.0002214462,0.0001522554,0.0001749695,0.0001475431],"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":[0.0002358101,0.0001159438,0.0008162521,0.0001165576,0.0001010127,0.000530676,0.00008857987,0.8683712,0.001611079,0.1082932,0.0007595843,0.01896011],"study_design_scores_gemma":[0.00002263089,0.0000293335,0.00008789277,0.00000368714,0.00001274111,0.00004995549,0.00002021264,0.9807301,0.0001474329,0.01867559,0.0002117048,0.000008762875],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07248959,0.0005423409,0.9223864,0.0002205058,0.00007340255,0.00008566966,0.00002972686,0.00008696094,0.004085403],"genre_scores_gemma":[0.9356688,0.0002488792,0.06215438,0.00005405441,0.00004715807,0.00007578619,0.00002689529,0.0000183531,0.001705514],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003887875,"threshold_uncertainty_score":0.02056128,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03298849325234046,"score_gpt":0.307144387752755,"score_spread":0.2741558945004146,"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."}}