{"id":"W2019936054","doi":"10.1109/tcomm.2012.12.100562","title":"Gaussian Multiple Description Coding with Low-Density Generator Matrix Codes","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Generator matrix; Gaussian; Coding (social sciences); Algorithm; Quadratic equation; Quantization (signal processing); Mathematics; Computer science; Discrete mathematics; Topology (electrical circuits); Combinatorics; Decoding methods; 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":[0.0001735773,0.0002349467,0.0002096386,0.0002062222,0.0006470828,0.00005458078,0.0006962163,0.0001610145,0.00003569311],"category_scores_gemma":[0.000006111162,0.0002415732,0.00007485341,0.0004037969,0.0001709574,0.0006719236,0.000008224433,0.0007597054,0.00008423429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002403033,"about_ca_system_score_gemma":0.00002033929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000398743,"about_ca_topic_score_gemma":0.0002865331,"domain_scores_codex":[0.9989201,0.0001305289,0.0003114202,0.0001403832,0.0001686928,0.0003288274],"domain_scores_gemma":[0.9971849,0.0002345676,0.00006695498,0.00228405,0.0001002421,0.0001292232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002265989,0.003984388,0.003450885,0.000412119,0.0008996641,0.000003178028,0.007323985,0.2561491,0.6034371,0.02110332,0.002177386,0.1008322],"study_design_scores_gemma":[0.00120815,0.0001167025,0.002551426,0.0003845157,0.0001832195,0.00006338948,0.0007353929,0.2097347,0.768833,0.000334708,0.0144882,0.001366586],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02889449,0.0006115822,0.9669839,0.0002163745,0.0001604941,0.000368551,0.00005458186,0.001610328,0.001099723],"genre_scores_gemma":[0.9046138,0.001657073,0.09311896,0.00005332509,0.00002125926,0.0003235869,0.00003139479,0.00007060415,0.000109979],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8757193,"threshold_uncertainty_score":0.9851068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02837938316599134,"score_gpt":0.2673096130151183,"score_spread":0.238930229849127,"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."}}