{"id":"W2128626239","doi":"10.1109/26.974248","title":"Hexacode-based quantization of the Gaussian source at 1/2 bit per sample","year":2001,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Binary Golay code; Decoding methods; Binary number; Quantization (signal processing); Algorithm; Gaussian; Bit (key); Algebraic number; Computer science; Mathematics; Binary code; Theoretical computer science; Discrete mathematics; Arithmetic; Physics","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":[0.0001569131,0.0001401655,0.0001401055,0.0001628644,0.0008446791,0.00004133207,0.002985646,0.00007118264,0.00008199969],"category_scores_gemma":[0.00002087261,0.0001135467,0.0001259322,0.0007988118,0.0002167414,0.0003442736,0.00005234387,0.0002689835,0.00003005785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009799614,"about_ca_system_score_gemma":0.00007288393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001425625,"about_ca_topic_score_gemma":0.0003293704,"domain_scores_codex":[0.9987091,0.0002572764,0.0003311577,0.0002482467,0.0002795994,0.0001746087],"domain_scores_gemma":[0.994418,0.0005927511,0.0001832704,0.004618898,0.0001257764,0.00006134657],"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":[0.0001771268,0.005819917,0.002542532,0.00009434523,0.0001933973,0.000001811759,0.003432771,0.3498317,0.1216751,0.0776019,0.006117708,0.4325117],"study_design_scores_gemma":[0.0007894066,0.000145274,0.001487238,0.0002133884,0.00005107716,0.00002319154,0.00006957746,0.5408109,0.2679493,0.004802635,0.18312,0.0005380177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008019872,0.00006269912,0.9933819,0.004356315,0.000123377,0.0002853459,0.000062582,0.0003439249,0.0005818764],"genre_scores_gemma":[0.82371,0.0001769424,0.1752314,0.0003470733,0.000005057636,0.00009104784,0.00001177458,0.00001591402,0.0004108587],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.822908,"threshold_uncertainty_score":0.6496674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03932801141443842,"score_gpt":0.2963049526388216,"score_spread":0.2569769412243832,"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."}}