{"id":"W2165488956","doi":"10.1109/tbme.2007.912421","title":"Respiratory Sounds Compression","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Advanced Data Compression Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Signal compression; Computer science; Data compression; Speech recognition; Adaptive filter; Signal processing; Adaptive coding; Compression ratio; Cluster analysis; Sensitivity (control systems); Coding (social sciences); SIGNAL (programming language); Noise (video); Pattern recognition (psychology); Artificial intelligence; Digital signal processing; Algorithm; Lossless compression; Mathematics; Electronic engineering; Statistics; Engineering; Computer hardware","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":[0.000104318,0.0001783797,0.0001705356,0.0003248393,0.0002044817,0.00002339138,0.0007147084,0.0001229666,0.00002743873],"category_scores_gemma":[0.00001088469,0.0001619708,0.0000773436,0.0005292146,0.00008720329,0.0004425164,0.00001013333,0.0004023186,0.00005546438],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006405286,"about_ca_system_score_gemma":0.00004247056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004176687,"about_ca_topic_score_gemma":1.919816e-7,"domain_scores_codex":[0.9985914,0.00002476061,0.0002621803,0.0003671534,0.0004626799,0.0002918294],"domain_scores_gemma":[0.998944,0.00009912885,0.00003799542,0.0006407476,0.00003233909,0.0002457844],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005811587,0.001470055,0.00001489925,0.0001225918,0.0001075704,0.0006189825,0.0007852828,0.08070038,0.5273473,0.004265749,0.01553367,0.3689755],"study_design_scores_gemma":[0.0009860223,0.0004096675,0.0002550831,0.0003068578,0.000008099031,0.0002108274,0.00000620481,0.4403265,0.3662579,0.0002715427,0.1901715,0.0007898356],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00163622,0.00006687223,0.9957821,0.0001325744,0.0007478868,0.00009560923,0.000008384219,0.001443022,0.0000873081],"genre_scores_gemma":[0.8708602,0.00006405226,0.1285338,0.0003074499,0.00006978477,0.00005404323,0.000001614672,0.00002351524,0.00008548686],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.869224,"threshold_uncertainty_score":0.6604976,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02241657292531462,"score_gpt":0.2497748937128848,"score_spread":0.2273583207875702,"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."}}