{"id":"W2154590881","doi":"10.1109/ccece.2007.343","title":"Quantitative Evaluation of Low Density Parity Check Convolutional Code Encoder and Decoder Algorithms for the XInC MIMD Multithreaded Microprocessor","year":2007,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; MIMD; Low-density parity-check code; Encoder; Microprocessor; Parallel computing; Algorithm; Convolutional code; Decoding methods; Computer hardware","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":[0.004418109,0.0001541732,0.0001936127,0.00008519242,0.0002230025,0.00004866843,0.0004420824,0.000101074,0.000007978182],"category_scores_gemma":[0.0007657251,0.0001143795,0.00006545455,0.0002440841,0.0001998933,0.0002929525,0.0001580168,0.0001370296,0.000002470748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009287611,"about_ca_system_score_gemma":0.0001837656,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002837108,"about_ca_topic_score_gemma":0.001865932,"domain_scores_codex":[0.9983528,0.00009102312,0.0003474327,0.0004146217,0.0005286752,0.0002654719],"domain_scores_gemma":[0.9966004,0.001238282,0.0002195131,0.0003503999,0.001535157,0.00005623949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001124559,0.002373971,0.07326641,0.000797142,0.000940702,0.00001004917,0.03276863,0.001962607,0.2412321,0.2796693,0.008790846,0.3570637],"study_design_scores_gemma":[0.0006118984,0.000100092,0.02902486,0.00002820881,0.00003990453,0.00001402689,0.0002625462,0.7935109,0.1668173,0.009380975,0.00004451218,0.0001647602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.176082,0.0002800235,0.8221304,0.0003860838,0.0001747795,0.000684121,0.000007441207,0.000129886,0.0001252118],"genre_scores_gemma":[0.5974439,0.000007306244,0.4023071,0.0001244701,0.00001870757,0.00003377611,0.00000269866,0.000006520046,0.00005544191],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7915483,"threshold_uncertainty_score":0.4664261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09775413545252984,"score_gpt":0.3820416279055111,"score_spread":0.2842874924529812,"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."}}