{"id":"W2155229197","doi":"10.1109/iembs.2001.1020592","title":"Steady-state and dynamic myoelectric signal compression using embedded zero-tree wavelets","year":2005,"lang":"en","type":"article","venue":"","topic":"Analog and Mixed-Signal Circuit Design","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Wavelet; Compression (physics); Data compression; Computer science; Wavelet transform; SIGNAL (programming language); Compression ratio; Speech recognition; Encoding (memory); Artificial intelligence; Pattern recognition (psychology); Computer vision; Engineering; 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":[0.000116137,0.0002237612,0.000232591,0.0001633398,0.0001020727,0.00004959175,0.0001068598,0.00008867291,0.0001830673],"category_scores_gemma":[0.000004092013,0.0002046991,0.00004955465,0.0001924143,0.00003276739,0.0001985628,0.00002075231,0.0002046765,0.00008166231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009956516,"about_ca_system_score_gemma":0.0000196302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001634557,"about_ca_topic_score_gemma":0.00002966037,"domain_scores_codex":[0.9989176,0.00003733025,0.000247333,0.000224453,0.0001705899,0.0004027199],"domain_scores_gemma":[0.9995951,0.0000577803,0.00003089773,0.0001431193,0.00003095116,0.0001421689],"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":[0.00002030223,0.00004830023,0.0001096475,0.00008013882,0.00009792127,0.00003696667,0.0004559342,0.09295179,0.624385,0.001168063,0.002124685,0.2785213],"study_design_scores_gemma":[0.000596354,0.00005533572,0.0004656074,0.00004532393,0.00003750447,0.00006356405,0.00004355919,0.9791908,0.01701809,0.001507422,0.0005765298,0.0003999552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3327231,0.0009710111,0.6586354,0.000004130973,0.00005242285,0.0001344322,0.000003872581,0.0002357177,0.007239915],"genre_scores_gemma":[0.9975864,0.00009043805,0.0009183495,0.00007055944,0.00004673796,0.000003944918,0.000007052845,0.00004468861,0.001231859],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.886239,"threshold_uncertainty_score":0.8347387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01183169029579269,"score_gpt":0.2216825703578207,"score_spread":0.209850880062028,"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."}}