{"id":"W2152831535","doi":"10.1109/isit.2008.4595488","title":"Optimal quantization for noisy channels with random index assignment","year":2008,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Quantization (signal processing); Algorithm; Vector quantization; Channel (broadcasting); Sigma; Computer science; Decoding methods; Coding (social sciences); Mathematics; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002591952,0.0007222046,0.001107628,0.0005078551,0.0004042599,0.001203857,0.0008670965,0.0007941414,0.001050968],"category_scores_gemma":[0.01189938,0.0005058715,0.000282768,0.0008334916,0.001455766,0.002252449,0.001187052,0.0009832536,0.0003112081],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001431578,"about_ca_system_score_gemma":0.001453223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002397661,"about_ca_topic_score_gemma":0.001791807,"domain_scores_codex":[0.997322,0.001126241,0.0001284971,0.000478395,0.0007078687,0.000236977],"domain_scores_gemma":[0.9953694,0.003142614,0.000513259,0.0003066084,0.0005782979,0.00008977998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003254287,0.00003251086,0.0005490306,0.0001400793,0.00003851555,0.0000620207,0.0001081222,0.8477194,0.006139051,0.0859018,0.0009036571,0.05808043],"study_design_scores_gemma":[0.00001854409,0.00003546748,0.00007175418,0.00001065128,0.000006027272,0.00001932951,0.000009896474,0.9740021,0.001481043,0.02391222,0.0004222791,0.00001074007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01152395,0.0003498625,0.9869871,0.0001400389,0.00002333717,0.00002160479,0.0000369859,0.0001071928,0.0008099717],"genre_scores_gemma":[0.790019,0.0007572339,0.2061374,0.0001773736,0.00009295576,0.0001421585,0.0001561739,0.00007147811,0.002446248],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002591952,"threshold_uncertainty_score":0.0137077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02465629865230943,"score_gpt":0.2683787751388403,"score_spread":0.2437224764865309,"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."}}