{"id":"W2137990712","doi":"10.1109/iscas.2005.1465635","title":"Architectures for ASIC Implementations of Low-Density Parity-Check Convolutional Encoders and Decoders","year":2005,"lang":"en","type":"article","venue":"","topic":"Error Correcting Code Techniques","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Application-specific integrated circuit; Encoder; Low-density parity-check code; Computer science; Convolutional code; Decoding methods; Throughput; Realization (probability); Serial concatenated convolutional codes; Architecture; Parallel computing; Computer architecture; Computer hardware; Block code; Algorithm; Concatenated error correction code; Mathematics; Telecommunications","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":[0.0002759133,0.00009665348,0.0001248169,0.0001063692,0.0001238086,0.0000302874,0.0002798168,0.00003771735,0.00001406089],"category_scores_gemma":[0.0000875257,0.00009358423,0.00005677002,0.0001171968,0.0000851016,0.00009678978,0.0001292355,0.00007476805,0.000001471575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003538013,"about_ca_system_score_gemma":0.00007291914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001294221,"about_ca_topic_score_gemma":0.001264547,"domain_scores_codex":[0.9991534,0.00003100838,0.0002168001,0.0002574064,0.0001496466,0.0001917811],"domain_scores_gemma":[0.9992321,0.0002879731,0.00008586339,0.0002305067,0.0001072972,0.00005626223],"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":[0.0001084373,0.000642357,0.1222043,0.0003410515,0.0002608919,0.00000329505,0.01272621,0.003011095,0.02639951,0.5191427,0.03639063,0.2787695],"study_design_scores_gemma":[0.002051502,0.0005276734,0.1009421,0.00007748215,0.00005121774,0.000091669,0.0007042412,0.4564549,0.2935721,0.1422808,0.002319758,0.0009265762],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2530355,0.0000267008,0.7446756,0.001238119,0.00005215361,0.0002331735,0.000005929367,0.0001955703,0.0005372298],"genre_scores_gemma":[0.6284161,0.000001863143,0.371212,0.0002856199,0.00001724207,0.00001825178,0.000002029157,0.000003650692,0.00004322211],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4534438,"threshold_uncertainty_score":0.3816254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01749115870873056,"score_gpt":0.3007548408460522,"score_spread":0.2832636821373216,"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."}}