{"id":"W2205827828","doi":"","title":"Near ML Performance for Linear Block Codes Using an Iterative Vector SISO Decoder","year":2006,"lang":"en","type":"article","venue":"Turbo Codes&Related Topics; 6th International ITG-Conference on Source and Channel Coding (TURBOCODING), 2006 4th International Symposium on","topic":"Coding theory and cryptography","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Code word; Decoding methods; Algorithm; Parity-check matrix; BCH code; Mathematics; Binary number; Block code; Computer science; Arithmetic","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0006352699,0.0006842677,0.0005184255,0.000496035,0.0008137582,0.001125787,0.00155502,0.0003550412,0.0001090388],"category_scores_gemma":[0.00007399054,0.0006637453,0.0002714735,0.0003583319,0.0002259991,0.001064538,0.0002773343,0.0006457322,0.0000346269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000273272,"about_ca_system_score_gemma":0.00009871852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001185128,"about_ca_topic_score_gemma":0.00002607672,"domain_scores_codex":[0.9960824,0.0001565352,0.0009041759,0.001271793,0.0008876618,0.000697498],"domain_scores_gemma":[0.997638,0.0002980391,0.0005376482,0.000534736,0.0007591421,0.0002324008],"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":[0.00134056,0.001963112,0.004624399,0.0001433262,0.0009944923,0.0000646278,0.006342507,0.2111572,0.01340044,0.7467379,0.00111354,0.01211789],"study_design_scores_gemma":[0.001815316,0.0007859252,0.0007513933,0.0005285179,0.00005895262,0.00008373719,0.0001168976,0.9620506,0.01405913,0.01052245,0.008265616,0.000961445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8724946,0.0001129196,0.1003634,0.006114384,0.00382658,0.0009764467,0.000253646,0.0005925852,0.01526543],"genre_scores_gemma":[0.991387,0.0001161168,0.003415355,0.0008661983,0.001116089,0.00009293685,0.0002121445,0.00006143372,0.002732733],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7508934,"threshold_uncertainty_score":0.9999111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02941576184409039,"score_gpt":0.271466253060888,"score_spread":0.2420504912167976,"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."}}