{"id":"W2153722101","doi":"10.1109/lcomm.2007.070097","title":"Outage Probability of Selection Cooperation in the Low to Medium SNR Regime","year":2007,"lang":"en","type":"article","venue":"IEEE Communications Letters","topic":"Cooperative Communication and Network Coding","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Outage probability; Signal-to-noise ratio (imaging); Selection (genetic algorithm); Computer science; Diversity combining; Maximal-ratio combining; Channel (broadcasting); Diversity gain; Diversity (politics); Mathematical optimization; Algorithm; Statistics; Applied mathematics; Mathematics; Telecommunications; Fading; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002543786,0.000112282,0.0001397846,0.0002030323,0.0002961577,0.00008601535,0.003348282,0.00004368235,0.000006005434],"category_scores_gemma":[0.000168866,0.00009555943,0.00004581486,0.001643101,0.0001307983,0.0003337385,0.0003381465,0.0003239457,0.00001967247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001235292,"about_ca_system_score_gemma":0.00005895712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000038087,"about_ca_topic_score_gemma":0.001653471,"domain_scores_codex":[0.9982715,0.0006220816,0.0004604226,0.0002146828,0.0002214118,0.000209965],"domain_scores_gemma":[0.9962917,0.00059325,0.0001239423,0.002731859,0.0002112506,0.00004800491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00006651294,0.001580949,0.01111156,0.00005914335,0.00006098215,0.000003871793,0.04974436,0.008042253,0.6400486,0.1725592,0.02372644,0.09299615],"study_design_scores_gemma":[0.004032204,0.000800369,0.4178433,0.000875323,0.00007140633,0.000107394,0.001546006,0.2037843,0.191098,0.003190131,0.1738919,0.002759665],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2308798,0.0001350951,0.6800568,0.08660462,0.0001655178,0.0008032076,0.000001264934,0.00009085275,0.001262804],"genre_scores_gemma":[0.9617646,0.00009864618,0.03287905,0.005126334,0.00002922275,0.00006781288,0.000006866009,0.000006139258,0.00002135766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7308847,"threshold_uncertainty_score":0.6221997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05252143764932973,"score_gpt":0.3080261926892231,"score_spread":0.2555047550398934,"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."}}