{"id":"W2138384328","doi":"10.1109/cwit.2011.5872126","title":"Hybrid digital-analog source-channel coding with one-to-three bandwidth expansion","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Bandwidth (computing); Computer science; Gaussian; Lossy compression; Channel code; Sub-band coding; Algorithm; Nonlinear system; Coding (social sciences); Electronic engineering; Decoding methods; Mathematics; Topology (electrical circuits); Speech recognition; Telecommunications; Speech coding; Physics; Engineering; Artificial intelligence","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":[0.0005672554,0.0003785359,0.0003988008,0.0002426903,0.0002622711,0.00039914,0.0006313106,0.0004508789,0.001701991],"category_scores_gemma":[0.001102931,0.0001086469,0.0001792516,0.000456676,0.0005478241,0.0008952304,0.0008075224,0.0004851678,0.0002343198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004195343,"about_ca_system_score_gemma":0.0003941529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008444472,"about_ca_topic_score_gemma":0.001151487,"domain_scores_codex":[0.9997026,0.00009033816,0.00001232923,0.00003871824,0.0001198873,0.00003608032],"domain_scores_gemma":[0.999297,0.0004399679,0.00007524745,0.00007117677,0.00008995108,0.00002660737],"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":[0.0007579896,0.0001327567,0.0009637873,0.0003856447,0.00006378467,0.0004537399,0.0002357862,0.5239205,0.09893285,0.1464035,0.001782788,0.2259668],"study_design_scores_gemma":[0.00002461631,0.00009570173,0.0001292802,0.00001151484,0.00001191841,0.0001378148,0.00001890799,0.9677603,0.01978842,0.01079927,0.001207753,0.00001459102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08119001,0.0005469991,0.9147073,0.0001687692,0.00003147287,0.00004548241,0.00005150299,0.0002342186,0.003024265],"genre_scores_gemma":[0.8972898,0.000261548,0.1000185,0.00007541071,0.00003149038,0.00005474136,0.00004229685,0.00001681763,0.002209482],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001701991,"threshold_uncertainty_score":0.005693674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0421843383897773,"score_gpt":0.2356113601470567,"score_spread":0.1934270217572794,"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."}}