{"id":"W2594525279","doi":"","title":"Implementation of the DVB-RCS turbo codes","year":2002,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Digital Video Broadcasting; Turbo equalizer; Turbo code; VHDL; Convolutional code; Decoding methods; Verilog; Field-programmable gate array; Viterbi decoder; Computer hardware; Soft-decision decoder; Algorithm; Parallel computing; Concatenated error correction code; Block code; 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":[0.000243129,0.0003190606,0.0002460315,0.0004733271,0.0002567552,0.0004337977,0.0006624648,0.0003205931,0.002820693],"category_scores_gemma":[0.0009295816,0.0001409137,0.0002319676,0.0003565627,0.000185313,0.0003481304,0.0002452701,0.000273687,0.001291923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005642626,"about_ca_system_score_gemma":0.001345717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002749486,"about_ca_topic_score_gemma":0.002826586,"domain_scores_codex":[0.9996619,0.00004502349,0.00002291326,0.00003366233,0.0001971683,0.00003933616],"domain_scores_gemma":[0.9996339,0.00004331472,0.00002982049,0.00006427113,0.0002146402,0.00001406872],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005940222,0.0001214562,0.002988789,0.001020767,0.0001224214,0.000850435,0.0003489569,0.2869954,0.23438,0.07095872,0.009287138,0.3923319],"study_design_scores_gemma":[0.00007910707,0.0005769485,0.001835117,0.00007219017,0.000062131,0.001099592,0.00005711355,0.6003171,0.3415597,0.004042344,0.05024692,0.00005169502],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1047095,0.0009180028,0.8543931,0.0002279954,0.0001860825,0.0003597026,0.0006280675,0.004535315,0.0340423],"genre_scores_gemma":[0.6512725,0.0005782491,0.3330441,0.0001133783,0.00003358005,0.0001915118,0.001124684,0.0002020913,0.01343989],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002820693,"threshold_uncertainty_score":0.00943619,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01552266689657512,"score_gpt":0.2585178827565963,"score_spread":0.2429952158600212,"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."}}