{"id":"W2170449768","doi":"10.1109/newcas.2007.4487987","title":"A new encoder implementation for low-density parity-check convolutional codes","year":2007,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Encoder; Low-density parity-check code; Computer science; Serial concatenated convolutional codes; Convolutional code; Turbo code; Field-programmable gate array; Raptor code; Concatenated error correction code; Block code; Parallel computing; Algorithm; Computer hardware; Decoding methods; Error floor","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":[0.0001832456,0.0003204916,0.0002379182,0.0003061768,0.000237835,0.0003975904,0.0005761448,0.0002854129,0.003035248],"category_scores_gemma":[0.000637628,0.0001425889,0.0001397072,0.0002327039,0.0001528757,0.0005970431,0.0002814351,0.0004729766,0.0006710737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003239117,"about_ca_system_score_gemma":0.0007057068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009590614,"about_ca_topic_score_gemma":0.002644961,"domain_scores_codex":[0.9998166,0.00001941859,0.00001317662,0.00002573087,0.0001006253,0.00002442878],"domain_scores_gemma":[0.9996904,0.00006232409,0.00002133666,0.00005965291,0.000148773,0.00001751827],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005012518,0.0001571892,0.002201642,0.0004318572,0.00007951532,0.0006527426,0.0001258587,0.02384539,0.3909404,0.03752306,0.008805057,0.534736],"study_design_scores_gemma":[0.0002223806,0.00104738,0.003053674,0.00008888732,0.0001370724,0.0027467,0.00004383764,0.4186783,0.5106829,0.004457689,0.05875551,0.00008558806],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07953929,0.0005430506,0.9062608,0.0002221066,0.0003256969,0.000227678,0.0003621661,0.003773354,0.008745803],"genre_scores_gemma":[0.4722765,0.0004865467,0.514253,0.0001826182,0.000106109,0.0001600084,0.0008252123,0.00009451353,0.01161554],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003035248,"threshold_uncertainty_score":0.01015389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02339645382462992,"score_gpt":0.3371124920444329,"score_spread":0.313716038219803,"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."}}