{"id":"W2164866926","doi":"10.1109/twc.2007.06020009","title":"Exact Error Rate Analysis of Output-Threshold Generalized Selection Combining (OT-GSC)","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Rayleigh fading; Selection (genetic algorithm); Probability density function; Diversity combining; Cumulative distribution function; Statistics; Mathematics; Maximal-ratio combining; Word error rate; Moment-generating function; Fading; Computer science; Algorithm; Applied mathematics; Speech recognition; Artificial intelligence; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007318884,0.0003490765,0.0006236717,0.001496187,0.0005025135,0.00003834277,0.001315258,0.00022981,0.00007112],"category_scores_gemma":[0.000009212636,0.0004138083,0.0003731221,0.003172773,0.0002654771,0.0003457344,0.00001479391,0.0008186714,0.0000170494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002901971,"about_ca_system_score_gemma":0.00003836972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001428732,"about_ca_topic_score_gemma":0.00152941,"domain_scores_codex":[0.9978245,0.0001965985,0.0009967264,0.0002894487,0.0002828036,0.0004098534],"domain_scores_gemma":[0.9958121,0.0006766128,0.0002438774,0.002840338,0.0002807613,0.0001463516],"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":[0.00008416036,0.0007345454,0.0003865748,0.00005628881,0.002070257,0.00000106268,0.001147717,0.8452924,0.09583235,0.005199397,0.0001369925,0.0490582],"study_design_scores_gemma":[0.0007400225,0.00009005635,0.002335249,0.0001008185,0.0009195877,0.000004246871,0.0003195549,0.7400522,0.2530305,0.0002565683,0.001488435,0.0006628426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1556772,0.0002402835,0.8402508,0.0001356,0.0001382695,0.0003641754,0.00006526352,0.001238825,0.00188951],"genre_scores_gemma":[0.9735756,0.001897214,0.02392096,0.00006793292,0.00001077294,0.0001848927,0.00008467227,0.0000924519,0.0001655083],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8178984,"threshold_uncertainty_score":0.9998314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03666324200033537,"score_gpt":0.3026396351502073,"score_spread":0.2659763931498719,"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."}}