{"id":"W2952423309","doi":"10.48550/arxiv.1110.4126","title":"Relay Selection and Performance Analysis in Multiple-User Networks","year":2011,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Relay; Computer science; Selection (genetic algorithm); Quadratic equation; Scheme (mathematics); Signal-to-noise ratio (imaging); Computer network; Mathematical optimization; Mathematics; Telecommunications; 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":[0.002471112,0.001251217,0.001120303,0.0009543307,0.0006115454,0.001657454,0.0009937524,0.001023999,0.002243004],"category_scores_gemma":[0.01215112,0.0005301198,0.0003978231,0.001719234,0.001349401,0.002218394,0.001138241,0.000792191,0.000499812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001776153,"about_ca_system_score_gemma":0.0007689302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002403801,"about_ca_topic_score_gemma":0.001218648,"domain_scores_codex":[0.9976521,0.001126065,0.00006810822,0.0002554379,0.0006659248,0.0002323268],"domain_scores_gemma":[0.9940243,0.00447775,0.0004594283,0.0002594966,0.000691471,0.00008763996],"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":[0.0000806083,0.00002074153,0.0006929979,0.0001334214,0.00003456052,0.0001879786,0.0001456524,0.8988495,0.002097066,0.08000031,0.00090065,0.01685654],"study_design_scores_gemma":[0.000005078123,0.00003889348,0.0001921278,0.00001366921,0.000009721219,0.00008842626,0.0000265518,0.9811543,0.0005166506,0.0173514,0.0005924155,0.00001090139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04553773,0.00398358,0.9371406,0.0005320077,0.00006820845,0.00006408893,0.0001026768,0.0002405492,0.01233059],"genre_scores_gemma":[0.9559754,0.003235443,0.03718441,0.0001027109,0.0001937282,0.0001131316,0.00008743306,0.00005271789,0.003055157],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002471112,"threshold_uncertainty_score":0.01306868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.087125536735113,"score_gpt":0.1926588275126897,"score_spread":0.1055332907775767,"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."}}