{"id":"W2014738898","doi":"10.1109/glocom.2011.6134058","title":"Buffers Improve the Performance of Relay Selection","year":2011,"lang":"en","type":"preprint","venue":"","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Relay; Selection (genetic algorithm); Diversity gain; Computer science; Network packet; Transmission (telecommunications); Relay channel; Outage probability; Control theory (sociology); Computer network; Telecommunications; Power (physics); Channel (broadcasting); Fading; Physics; Artificial intelligence","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":[0.002302588,0.001498602,0.0008563139,0.0006501228,0.0004996965,0.001568567,0.001323768,0.0007815232,0.002116654],"category_scores_gemma":[0.01073504,0.0004192507,0.0002863879,0.0005980745,0.0009824918,0.002568722,0.001452482,0.0007454139,0.0004765356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008913446,"about_ca_system_score_gemma":0.000945733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009360852,"about_ca_topic_score_gemma":0.0008348591,"domain_scores_codex":[0.9986865,0.0004815169,0.00008127489,0.0002136987,0.0002282905,0.0003086032],"domain_scores_gemma":[0.9901788,0.006586809,0.0008676576,0.00135086,0.0008098399,0.0002059624],"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":[0.002264004,0.0002956088,0.005439121,0.0003385821,0.000164719,0.0005990028,0.0004147735,0.7184029,0.08771575,0.07665912,0.003848198,0.1038582],"study_design_scores_gemma":[0.00008704903,0.0006185415,0.0008875648,0.00003354702,0.00009207557,0.0004035904,0.0000760113,0.9027066,0.07325485,0.01853931,0.003230876,0.00006996388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3610362,0.003202018,0.6214448,0.0007521901,0.0002727262,0.00008894663,0.0002801876,0.002797798,0.01012516],"genre_scores_gemma":[0.9773026,0.0004595157,0.02102452,0.00009616608,0.0000351141,0.00002456182,0.00005351249,0.00004541455,0.0009587237],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002302588,"threshold_uncertainty_score":0.01217741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04136997729331115,"score_gpt":0.2622272144017093,"score_spread":0.2208572371083981,"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."}}