{"id":"W2163291063","doi":"10.1109/lcomm.2007.070065","title":"Performance Analysis of Decode-and-Forward Relaying with Selection Combining","year":2007,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":121,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Selection (genetic algorithm); Relay; Outage probability; Diversity combining; Maximal-ratio combining; Signal-to-noise ratio (imaging); Expression (computer science); Antenna diversity; Cooperative diversity; Performance improvement; Algorithm; Telecommunications; Decoding methods; Fading; Wireless; Artificial intelligence; Power (physics); 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":[0.002575703,0.001710664,0.001282211,0.00112665,0.0007016791,0.001546884,0.0008724973,0.001407031,0.001755487],"category_scores_gemma":[0.01009603,0.0004463045,0.0005173452,0.001558281,0.001323395,0.001305193,0.001216888,0.0006976092,0.0006729208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00218404,"about_ca_system_score_gemma":0.001139211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003658494,"about_ca_topic_score_gemma":0.002478048,"domain_scores_codex":[0.9967352,0.001176302,0.0001047051,0.0002617491,0.001215827,0.0005062504],"domain_scores_gemma":[0.9903654,0.006889852,0.0007816318,0.0004497978,0.001395345,0.0001178423],"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":[0.0004934237,0.00005039424,0.00277438,0.0001821932,0.0001257269,0.0004143363,0.0002354212,0.9212539,0.01452745,0.03406304,0.0008597001,0.02501997],"study_design_scores_gemma":[0.00002146705,0.0002224853,0.00118281,0.00002245396,0.0000678716,0.00032341,0.00004797063,0.9853511,0.00523261,0.006871901,0.0006221074,0.00003375316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3315047,0.005010128,0.6128821,0.001172236,0.00008793123,0.0001267673,0.0004822985,0.0009321995,0.04780154],"genre_scores_gemma":[0.985607,0.001118728,0.01093542,0.00006132766,0.00007475796,0.00004347244,0.0001362073,0.00004842357,0.001974623],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003658494,"threshold_uncertainty_score":0.01584637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03319653540750594,"score_gpt":0.2830231413289397,"score_spread":0.2498266059214337,"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."}}