{"id":"W1931709921","doi":"","title":"High-throughput LDPC decoding using the RHS algorithm","year":2012,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; McGill University","funders":"","keywords":"Low-density parity-check code; Computer science; Decoding methods; Throughput; Algorithm; Application-specific integrated circuit; Code word; CMOS; Clock rate; Bit error rate; Latency (audio); Soft-decision decoder; Parallel computing; Computer engineering; Computer hardware; Wireless; Electronic engineering; Chip; Telecommunications; Engineering","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":[0.0004378129,0.0004102536,0.0004356688,0.0005899615,0.0003823429,0.0006888841,0.0005449737,0.0004946897,0.002893739],"category_scores_gemma":[0.001690771,0.0002079237,0.0002272147,0.0007622801,0.0004129847,0.000613186,0.0005930094,0.0005022264,0.001628898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006670201,"about_ca_system_score_gemma":0.00133236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003831739,"about_ca_topic_score_gemma":0.004958534,"domain_scores_codex":[0.9992585,0.0001445211,0.00003565212,0.00008115224,0.0004166623,0.00006356065],"domain_scores_gemma":[0.999286,0.0002471761,0.00004881308,0.0001961377,0.0002035788,0.00001833954],"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":[0.00036307,0.0001233696,0.001247979,0.0002170665,0.00006760956,0.0003679296,0.0002440749,0.4365972,0.1304999,0.07235871,0.007459452,0.3504536],"study_design_scores_gemma":[0.00003589939,0.00003251251,0.0002240804,0.00001372442,0.000007282137,0.0001016643,0.000008290882,0.9461379,0.04275581,0.006612795,0.004054367,0.00001570838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03311774,0.0001741051,0.9494284,0.0001904734,0.00002956569,0.00007266452,0.0002604021,0.004238479,0.01248817],"genre_scores_gemma":[0.4589582,0.0002171492,0.5306222,0.00009395312,0.00002976148,0.000127006,0.0005667185,0.0001910217,0.009194043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003831739,"threshold_uncertainty_score":0.009680569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02138624387528731,"score_gpt":0.2637628547762321,"score_spread":0.2423766109009448,"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."}}