{"id":"W2552101656","doi":"10.1109/allerton.2009.5394496","title":"Interactive encoding and decoding based on syndrome accumulation over binary LDPC ensembles: Universality and rate-adaptivity","year":2009,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Low-density parity-check code; Decoding methods; Algorithm; Encoder; Binary number; Computer science; Distributed source coding; Universality (dynamical systems); Mathematics; Theoretical computer science; Channel code; Statistics; Arithmetic","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":[0.002656755,0.0009043503,0.0009828373,0.0008116343,0.0004849015,0.0009644975,0.001034797,0.0007662136,0.0009832203],"category_scores_gemma":[0.01145211,0.0003039632,0.0004795225,0.000900215,0.001859279,0.002330035,0.002246356,0.001010318,0.0002289042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001122492,"about_ca_system_score_gemma":0.001006957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001153173,"about_ca_topic_score_gemma":0.001167521,"domain_scores_codex":[0.9978169,0.0007359127,0.0001216714,0.0002455456,0.000663885,0.0004161007],"domain_scores_gemma":[0.9906412,0.006450535,0.0008153886,0.001282237,0.00058271,0.0002279549],"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":[0.0006300631,0.0001138528,0.004174555,0.0001616361,0.0001270038,0.0003234288,0.0003697816,0.8119392,0.02941253,0.09979317,0.0002435737,0.05271119],"study_design_scores_gemma":[0.00001946899,0.000103511,0.0003650413,0.00001559424,0.0000203748,0.0001222383,0.00002577859,0.9597104,0.0258981,0.01342356,0.0002697698,0.00002599329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3118292,0.0005054804,0.6827407,0.0001690321,0.0000178592,0.00007119149,0.00008229833,0.0004865427,0.004097717],"genre_scores_gemma":[0.9468174,0.0001923056,0.05207006,0.00003371348,0.00001064676,0.00004443122,0.0000586235,0.00002783141,0.0007449975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002656755,"threshold_uncertainty_score":0.01405042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03584734140875678,"score_gpt":0.307292230030714,"score_spread":0.2714448886219572,"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."}}