{"id":"W2135146221","doi":"10.1109/tvt.2007.907075","title":"Adaptive Hybrid ARQ Systems With BCJR Decoding","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Hybrid automatic repeat request; Retransmission; Algorithm; Computer science; Turbo code; Decoding methods; Additive white Gaussian noise; BCJR algorithm; Low-density parity-check code; Throughput; Channel (broadcasting); Mathematics; Telecommunications link; Wireless; Error floor; Transmission (telecommunications); Computer network; Telecommunications","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":[0.00004776135,0.0002323005,0.0002695044,0.0005225939,0.0002404642,0.000009277791,0.0003547849,0.0001923501,0.00001019039],"category_scores_gemma":[0.000002262132,0.0002294897,0.00005808825,0.0005321023,0.0002216764,0.0001479252,0.000002119093,0.0006445486,0.00005174187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000171624,"about_ca_system_score_gemma":0.00002022473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000113043,"about_ca_topic_score_gemma":0.00001204244,"domain_scores_codex":[0.99905,0.00002567224,0.0002410183,0.0002408795,0.0001529995,0.000289448],"domain_scores_gemma":[0.9989923,0.00004741122,0.00004700543,0.0007814221,0.00008051324,0.00005139686],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003077427,0.00011322,0.00002888006,0.00003470856,0.0001895862,0.0001455459,0.00008150828,0.9590989,0.01265263,0.001528167,0.0001431354,0.02595289],"study_design_scores_gemma":[0.0005965639,0.0003310124,0.00001554136,0.0001773439,0.0000421176,0.001253787,0.0002414211,0.2350266,0.7576395,0.0003493199,0.003716265,0.0006105872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08638809,0.000470749,0.9081299,0.00009089478,0.0001240442,0.0003156892,0.00001231774,0.004019895,0.0004483913],"genre_scores_gemma":[0.9785007,0.0007955021,0.0201647,0.00001715306,0.000009884592,0.0003679534,0.000002145246,0.00007172801,0.00007027254],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8921126,"threshold_uncertainty_score":0.9358317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01426752827737562,"score_gpt":0.2072830795223703,"score_spread":0.1930155512449947,"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."}}