{"id":"W2342309445","doi":"10.22215/etd/2005-06505","title":"Iterative decoding in analog VLSI","year":2005,"lang":"en","type":"dissertation","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Decoding methods; Computer science; Sequential decoding; Very-large-scale integration; CMOS; Algorithm; Modular design; Electronic engineering; Iterative method; Block code; Engineering; Embedded system","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":[0.000185127,0.0002563393,0.0002428572,0.0003386179,0.0002550064,0.0008531867,0.0003167747,0.0004551896,0.003562746],"category_scores_gemma":[0.001282323,0.0002233305,0.0001855188,0.0005541162,0.0003375483,0.0005042264,0.0004164742,0.0004106919,0.0008361026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006931337,"about_ca_system_score_gemma":0.0007454594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001978252,"about_ca_topic_score_gemma":0.003414127,"domain_scores_codex":[0.9997459,0.000065702,0.00001313965,0.00003594608,0.0001101299,0.00002921543],"domain_scores_gemma":[0.999728,0.0001443851,0.00001650427,0.0000294377,0.00007241288,0.000009234325],"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":[0.0001878185,0.00005321799,0.001110169,0.0001884747,0.00006114346,0.0003622964,0.0002704357,0.5211964,0.03503448,0.1946551,0.004852909,0.2420277],"study_design_scores_gemma":[0.00003193031,0.00008771994,0.0003723835,0.00004462914,0.00001834254,0.000249258,0.00003484662,0.9101794,0.01509077,0.06419811,0.009673204,0.00001935604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07358521,0.002336015,0.8750753,0.0005967924,0.0002178846,0.0000615451,0.0001405488,0.001035259,0.04695149],"genre_scores_gemma":[0.801247,0.001897302,0.1429006,0.0002474231,0.0001292054,0.00006451315,0.0001529019,0.0001117009,0.05324935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003562746,"threshold_uncertainty_score":0.0119186,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0149438813896406,"score_gpt":0.3083961886925919,"score_spread":0.2934523073029514,"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."}}