{"id":"W2019751845","doi":"10.1049/el:20030217","title":"Iterative decoding using stochastic computation","year":2003,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":205,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Decoding methods; Computation; Computer science; Sequential decoding; Algorithm; List decoding; Code (set theory); Simple (philosophy); Berlekamp–Welch algorithm; Iterative method; Theoretical computer science; Concatenated error correction code; Set (abstract data type)","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":[0.0003290587,0.0001454049,0.0001251318,0.0001511578,0.0001816959,0.0001535859,0.0003249333,0.00003774406,0.000002536705],"category_scores_gemma":[0.00008676691,0.0001610604,0.00004952767,0.0004136601,0.00002162942,0.0003893558,0.00004757885,0.000251178,0.000009407634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003485419,"about_ca_system_score_gemma":0.00009714111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006148462,"about_ca_topic_score_gemma":0.000006229692,"domain_scores_codex":[0.9987336,0.0001359784,0.0001759172,0.0003367961,0.0001953363,0.0004223732],"domain_scores_gemma":[0.9994257,0.0001302182,0.0001060137,0.0002408444,0.00005134994,0.00004588016],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000119911,0.0001221398,0.0005198676,0.00002745786,0.000107176,0.00005539807,0.005582365,0.1539319,0.5482169,0.2650059,0.00262364,0.02379529],"study_design_scores_gemma":[0.000279349,0.0001162035,0.00003461078,0.00004640519,0.00001284562,0.0001505682,0.00002001986,0.93949,0.04863856,0.01036577,0.0003720768,0.0004735575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.145901,0.0001234139,0.8526586,0.0004045493,0.0002308088,0.0001251994,1.308973e-7,0.000360238,0.0001960173],"genre_scores_gemma":[0.7877596,0.000001199231,0.2111505,0.001041037,0.00002084783,0.000006079749,7.690546e-7,0.00001324384,0.000006705435],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7855582,"threshold_uncertainty_score":0.656785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01957735228012552,"score_gpt":0.2767375105798688,"score_spread":0.2571601582997433,"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."}}