{"id":"W2169481897","doi":"10.1109/glocom.2008.ecp.819","title":"Cross-Layer Design of Optimal Adaptation Technique over Selection-Combining Diversity Nakagami-m Fading Channels","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Network Optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Fading; Computer science; Nakagami distribution; Link adaptation; Transmission (telecommunications); Throughput; Channel (broadcasting); Markov process; Computer network; Bit error rate; Network packet; Markov decision process; Algorithm; Wireless; Telecommunications; Mathematics; Statistics","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":[0.000112835,0.0001477216,0.0001680392,0.0001369729,0.0002464741,0.0000119368,0.0001003007,0.0001158087,0.0001121316],"category_scores_gemma":[0.00001863753,0.0001708798,0.00004158012,0.0003920868,0.00004181966,0.0005209605,0.00006007757,0.0001347456,0.000004895242],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000115896,"about_ca_system_score_gemma":0.00001469852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002311417,"about_ca_topic_score_gemma":0.00000131247,"domain_scores_codex":[0.9992013,0.00002338826,0.0002216434,0.0001669709,0.0001682127,0.0002184952],"domain_scores_gemma":[0.9996215,0.00006923347,0.00006184672,0.0001008448,0.0001022257,0.00004434394],"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":[0.00001754992,0.00001274145,0.002357374,0.00001458005,0.00002169921,0.000002426183,0.0006335294,0.988205,0.008291434,0.00009598174,0.0000975014,0.0002502149],"study_design_scores_gemma":[0.000274483,0.00003785128,0.00109738,0.00002187955,0.000006947039,0.00001279337,0.00002813508,0.9423865,0.055897,0.00004794382,0.00001391419,0.0001751863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1250507,0.00002808941,0.8735859,0.000001355719,0.0001379753,0.0002601192,0.000001209989,0.0004542129,0.0004804785],"genre_scores_gemma":[0.8230425,0.00006594516,0.1766291,0.000006439919,0.00004219466,0.00002280822,0.000006315885,0.00003005384,0.0001546057],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6979918,"threshold_uncertainty_score":0.6968277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03253235457403451,"score_gpt":0.2412529126411166,"score_spread":0.2087205580670821,"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."}}