{"id":"W2108092828","doi":"10.1016/s1350-4533(03)00118-8","title":"Myoelectric signal compression using zero-trees of wavelet coefficients","year":2003,"lang":"en","type":"article","venue":"Medical Engineering & Physics","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Wavelet; Lossy compression; Data compression; Computer science; Biorthogonal wavelet; Compression (physics); Algorithm; Biorthogonal system; Wavelet transform; Pattern recognition (psychology); Mathematics; Artificial intelligence; Materials science","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":[0.0001115376,0.0001759004,0.000254638,0.00009732739,0.00004447872,0.000008563629,0.0001223302,0.00008119886,0.00005118303],"category_scores_gemma":[0.00007861751,0.0001702612,0.0000823118,0.0006170778,0.00003458039,0.0000617823,0.00001712317,0.0002421903,0.000001276815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004038321,"about_ca_system_score_gemma":0.00002467628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005205773,"about_ca_topic_score_gemma":2.406324e-7,"domain_scores_codex":[0.9988406,0.0000163055,0.0002465913,0.0001303274,0.0004519164,0.0003142391],"domain_scores_gemma":[0.9995823,0.00009037197,0.00003191153,0.0001338191,0.00004281654,0.0001188061],"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":[0.000008125666,0.0002511269,0.0005895794,0.0003537609,0.0002932414,0.000009026744,0.0003343624,0.7446654,0.1584201,0.005770638,0.001329416,0.08797523],"study_design_scores_gemma":[0.0005516776,0.00005204415,0.00140084,0.0001734364,0.00002793112,0.000005594486,0.00001178751,0.8990391,0.09482342,0.0001482109,0.00345123,0.0003147348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.280885,0.0003759382,0.7175598,0.000007236528,0.0002460916,0.00008591411,0.000002642841,0.0002299273,0.0006075388],"genre_scores_gemma":[0.998302,0.0000538708,0.001508749,0.00001855817,0.00006683185,0.000005756817,0.000004578712,0.00003350184,0.000006164511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7174171,"threshold_uncertainty_score":0.6943049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0104525596707215,"score_gpt":0.2129721598974359,"score_spread":0.2025196002267144,"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."}}