{"id":"W2156078113","doi":"10.1109/iscas.2008.4542111","title":"A 600-Mb/s encoder and decoder for low-density parity-check convolutional codes","year":2008,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Encoder; Soft-decision decoder; Computer science; Convolutional code; Throughput; Low-density parity-check code; Decoding methods; Code rate; Computer hardware; Parity bit; Algorithm; Real-time computing; Electronic engineering; Engineering; Telecommunications; Wireless","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":[0.0003714757,0.0001689258,0.0002170048,0.00008693222,0.0003473688,0.00006446132,0.0004320465,0.0001081483,0.00001545318],"category_scores_gemma":[0.0001750841,0.0001578746,0.00007454439,0.0001605562,0.0001535852,0.0003822924,0.0002882718,0.0001438945,0.00001600101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005086786,"about_ca_system_score_gemma":0.0001243226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001440636,"about_ca_topic_score_gemma":0.0001952903,"domain_scores_codex":[0.9986696,0.00004652748,0.0002277151,0.0004981702,0.0002334051,0.0003246],"domain_scores_gemma":[0.9988312,0.0003354604,0.00007346515,0.0004133486,0.0002317913,0.0001147779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001539131,0.0009652854,0.2544076,0.000244318,0.0001952761,0.000130301,0.00645028,0.00009257709,0.0153487,0.5080168,0.1985902,0.01540477],"study_design_scores_gemma":[0.002275257,0.0005977253,0.1444669,0.000119993,0.00003915699,0.001636626,0.0001333371,0.6147103,0.1083205,0.1142593,0.01154707,0.001893875],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1700454,0.00007987685,0.8263451,0.0006598075,0.0001892992,0.0002673885,0.000003122423,0.0007440927,0.001665864],"genre_scores_gemma":[0.665543,0.00002069439,0.3328635,0.0006460574,0.00004231303,0.00003508685,0.000002195236,0.000008720721,0.0008384074],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6146177,"threshold_uncertainty_score":0.6437941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02781198529244345,"score_gpt":0.2647599867677619,"score_spread":0.2369480014753185,"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."}}