{"id":"W1996784277","doi":"10.1109/ccece.2008.4564608","title":"Two channel estimation methods for amplify-and-forward relay networks","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Relay; Channel (broadcasting); Quantization (signal processing); Computer science; Relay channel; Estimator; Terminal (telecommunication); Algorithm; Telecommunications; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003423102,0.0003132237,0.0003468787,0.0003390667,0.0004692852,0.0004210265,0.0006114405,0.0001358913,0.000005406931],"category_scores_gemma":[0.00008862816,0.0003143568,0.0000473538,0.000505745,0.00006368692,0.0004111845,0.0001582288,0.0004548771,0.000002766407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001062295,"about_ca_system_score_gemma":0.0002371068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001668448,"about_ca_topic_score_gemma":0.0001174003,"domain_scores_codex":[0.9983327,0.00002761515,0.000297664,0.0005892167,0.0001225599,0.0006302277],"domain_scores_gemma":[0.9985656,0.0002078323,0.00008114753,0.0002087854,0.0003473587,0.0005892902],"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":[0.00000774577,0.00001580287,0.00008365046,0.00002040196,0.0000266866,0.000002909356,0.0006903238,0.002598785,0.0001241245,0.5951141,0.0003196615,0.4009958],"study_design_scores_gemma":[0.0003681862,0.0002134225,0.001077597,0.0000750721,0.000007917871,0.00007193198,0.00000548315,0.9943978,0.00008182841,0.001313812,0.002000944,0.00038602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002708754,0.0004059398,0.9943835,0.001317662,0.000162908,0.0003610115,0.000001518917,0.0001917808,0.0004669565],"genre_scores_gemma":[0.8407797,0.0008783535,0.1574807,0.0005670966,0.000107667,0.0001062628,0.00000649653,0.00001763975,0.00005609743],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.991799,"threshold_uncertainty_score":0.9999309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04092668455162749,"score_gpt":0.2804431612591722,"score_spread":0.2395164767075447,"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."}}