{"id":"W2076175929","doi":"10.1109/wcnc.2010.5506120","title":"Adaptive Multiple Relay Selection Scheme for Cooperative Wireless Networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Moment-generating function; Relay; Independent and identically distributed random variables; Rayleigh fading; Fading; Probability density function; Cumulative distribution function; Maximal-ratio combining; Upper and lower bounds; Topology (electrical circuits); Computer science; Wireless; Signal-to-noise ratio (imaging); Algorithm; Wireless network; Ergodic theory; Random variable; Mathematics; Cooperative diversity; Statistics; Telecommunications; Combinatorics; Decoding methods","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":[0.0003349076,0.0001419484,0.0001435314,0.0000570029,0.0004939358,0.0001582689,0.0006631753,0.00009730468,0.00005040196],"category_scores_gemma":[0.00009549865,0.000124737,0.00005898907,0.0004707747,0.00005260505,0.0004636894,0.0002241164,0.0003805202,0.00001952779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002954706,"about_ca_system_score_gemma":0.00006859729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006801901,"about_ca_topic_score_gemma":0.0007750226,"domain_scores_codex":[0.9990373,0.0000789715,0.0001939157,0.0003370419,0.0000997633,0.0002530203],"domain_scores_gemma":[0.9985374,0.0003884916,0.00006870976,0.0004410769,0.000475222,0.00008906906],"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":[0.00004295667,0.0001306906,0.001132554,0.000002680041,0.00004756051,5.268834e-7,0.0006278692,0.004076086,0.02628783,0.8553713,0.008235055,0.1040449],"study_design_scores_gemma":[0.0004571282,0.00009469176,0.0004380622,0.00000709142,0.000002182235,0.000004157248,0.0000235159,0.984759,0.004358966,0.00007222935,0.00960207,0.0001808802],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01429892,0.00004182786,0.9818681,0.0006923836,0.000438682,0.0004213752,8.358725e-7,0.0002599163,0.00197794],"genre_scores_gemma":[0.8360112,0.00005272027,0.162092,0.0004371286,0.000137126,0.0001211334,0.000005543129,0.00001028663,0.001132896],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9806829,"threshold_uncertainty_score":0.5086628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03168248471921244,"score_gpt":0.2728310737852176,"score_spread":0.2411485890660051,"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."}}