{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001773746,0.0007634156,0.0009763458,0.0007565673,0.0007446063,0.00124122,0.001982423,0.001142841,0.001937861],"category_scores_gemma":[0.003970212,0.0004377369,0.0004195121,0.0009313311,0.000906374,0.001349635,0.001296692,0.0009542288,0.001034014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006083649,"about_ca_system_score_gemma":0.0007759635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002267213,"about_ca_topic_score_gemma":0.002501376,"domain_scores_codex":[0.9974818,0.0007997104,0.0001375518,0.0003778902,0.001029547,0.0001735769],"domain_scores_gemma":[0.9962165,0.001709156,0.0003483456,0.0006777388,0.0009678292,0.00008041933],"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":[0.001036135,0.0003334336,0.002230139,0.0003112031,0.0002615193,0.0004563557,0.0006829077,0.4954286,0.08805524,0.04130975,0.003701662,0.3661931],"study_design_scores_gemma":[0.00008022306,0.0002108139,0.000187044,0.000009025714,0.00002706933,0.0001367313,0.00002238701,0.9810941,0.01251653,0.004053649,0.001622094,0.00004031327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06484837,0.0005673231,0.9277367,0.000169154,0.00009790462,0.0001563724,0.00008170523,0.002115269,0.00422725],"genre_scores_gemma":[0.6014216,0.0002257755,0.3941713,0.0001655455,0.0001343822,0.0001642597,0.0001198435,0.00007464339,0.003522751],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002267213,"threshold_uncertainty_score":0.009380579,"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."}}