{"id":"W4300281352","doi":"10.48550/arxiv.1501.04199","title":"Distributed Resource Allocation in D2D-Enabled Multi-tier Cellular\\n Networks: An Auction Approach","year":2015,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Resource allocation; Scalability; Computer network; Distributed computing; Resource management (computing); Scheme (mathematics); Heterogeneous network; Throughput; Interference (communication); Wireless network; Radio resource management; Wireless; Channel (broadcasting); Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0009328258,0.001125491,0.001021092,0.0007173473,0.0002881579,0.0001638401,0.001097559,0.001587907,0.00003598092],"category_scores_gemma":[0.00006605526,0.001610353,0.0002440738,0.002926189,0.0002229718,0.001399287,0.0006466287,0.002039579,0.00003840753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003133486,"about_ca_system_score_gemma":0.0001912312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002127926,"about_ca_topic_score_gemma":0.0001466965,"domain_scores_codex":[0.9945859,0.0007364368,0.0009398495,0.002298424,0.0002291245,0.001210205],"domain_scores_gemma":[0.9963357,0.00009169109,0.0006475223,0.001841971,0.0005131763,0.0005698691],"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":[0.0003389049,0.0005606293,0.001443657,0.000200343,0.0001486186,0.00009546128,0.0004790176,0.9946658,0.0000731012,0.001165876,0.0001549433,0.0006736365],"study_design_scores_gemma":[0.002840585,0.00007395558,0.0006199756,0.000258327,0.0002455737,0.000005435041,0.001115183,0.9921042,0.00007822752,0.0006263921,0.0005620816,0.001470064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1014925,0.0003692469,0.894617,0.00001133847,0.0007796136,0.001465084,0.00004904293,0.0005969501,0.0006192494],"genre_scores_gemma":[0.9840812,0.0009437719,0.00816578,0.00001445122,0.0004771669,0.00001582916,0.00520482,0.0002388375,0.0008581293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8864511,"threshold_uncertainty_score":0.9997082,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05347831925146664,"score_gpt":0.1791337910625821,"score_spread":0.1256554718111155,"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."}}