{"id":"W2171685173","doi":"10.1109/glocom.2012.6504036","title":"Improving throughput by fine-grained channel allocation in cooperative wireless networks","year":2012,"lang":"en","type":"article","venue":"","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computer network; Relay; Throughput; Channel (broadcasting); Node (physics); Channel allocation schemes; Wireless network; Wireless; Cooperative diversity; Antenna diversity; Diversity gain; Cognitive radio; Transmission (telecommunications); Relay channel; Distributed computing; Fading; Telecommunications; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.0005728627,0.0001638134,0.0001706827,0.00007101864,0.000191421,0.0001288453,0.000709109,0.00007202642,0.00003248344],"category_scores_gemma":[0.00004421165,0.0001466377,0.00003220452,0.0007202412,0.00003865971,0.001093799,0.0004090239,0.0002264192,0.00002564037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008634274,"about_ca_system_score_gemma":0.00003676819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004576846,"about_ca_topic_score_gemma":0.000249041,"domain_scores_codex":[0.998701,0.0002084326,0.0002749385,0.0002651068,0.0001333425,0.0004171981],"domain_scores_gemma":[0.9990091,0.0001430445,0.00008054587,0.0005408706,0.0001225762,0.0001038454],"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":[0.00002321865,0.0007428779,0.003111332,0.00001824279,0.00004688623,0.000002286088,0.009549863,0.006451895,0.01307582,0.5175364,0.03048343,0.4189578],"study_design_scores_gemma":[0.0004257137,0.00003443212,0.0007923323,0.00002354204,0.000001901558,0.000002657267,0.00008440486,0.9942987,0.002473262,0.00004347351,0.001558989,0.0002605955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008995598,0.001260463,0.9845146,0.001870968,0.0002792536,0.0002632852,5.339508e-7,0.0001566717,0.00265865],"genre_scores_gemma":[0.9947786,0.0003133274,0.002972906,0.000985229,0.000105258,0.00006045132,0.00001830234,0.00001067128,0.000755234],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9878468,"threshold_uncertainty_score":0.5979712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02676011794822166,"score_gpt":0.2648551014447135,"score_spread":0.2380949834964919,"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."}}