{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001563501,0.0005207498,0.0007582443,0.0005582176,0.0004998115,0.0006935966,0.0009055789,0.0005868239,0.0005543478],"category_scores_gemma":[0.005262765,0.0002998266,0.0001995498,0.0009037137,0.001167658,0.001414584,0.001269013,0.0005241098,0.0001120367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009441854,"about_ca_system_score_gemma":0.0008039227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002125266,"about_ca_topic_score_gemma":0.002535636,"domain_scores_codex":[0.9991471,0.0003578629,0.0000343331,0.0001119318,0.0001403843,0.0002084636],"domain_scores_gemma":[0.9970965,0.001964443,0.0002923907,0.0004000457,0.0001545014,0.00009209453],"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":[0.00006872448,0.00005411233,0.0004634681,0.00003151466,0.00001094377,0.00004784724,0.00007617632,0.9646016,0.00515424,0.007635758,0.0003582538,0.02149725],"study_design_scores_gemma":[0.00001089108,0.00004094222,0.0001537399,0.000002571101,0.000004674374,0.0000269361,0.00001950252,0.9912601,0.001102534,0.007155463,0.0002160956,0.000006480665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2043545,0.0004138512,0.7922752,0.000187838,0.00002561858,0.0000646138,0.00004439693,0.000420143,0.002213866],"genre_scores_gemma":[0.9675522,0.0001247747,0.03185147,0.00002856668,0.00001257259,0.00004116298,0.00001508888,0.00001369122,0.0003604463],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002125266,"threshold_uncertainty_score":0.008268714,"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."}}