{"id":"W1487475484","doi":"10.1109/iembs.2006.259353","title":"Automated Extraction of Swallowing Sounds Using a Wavelet-Based Filter","year":2006,"lang":"en","type":"article","venue":"","topic":"Voice and Speech Disorders","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer science; Wavelet; Extraction (chemistry); Swallowing; Filter (signal processing); Speech recognition; Wavelet transform; Artificial intelligence; Pattern recognition (psychology); Computer vision; Medicine","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":[0.0006515277,0.0004841633,0.0006343304,0.00147695,0.0001957385,0.0005844323,0.0004532933,0.0008381272,0.001408707],"category_scores_gemma":[0.001474316,0.0002919916,0.0004837091,0.0006136114,0.0002589453,0.0007193753,0.0003142491,0.000345501,0.001381703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001584892,"about_ca_system_score_gemma":0.0003252993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004145344,"about_ca_topic_score_gemma":0.000618632,"domain_scores_codex":[0.9995685,0.00006208449,0.00003242268,0.00008858692,0.0002203372,0.00002810738],"domain_scores_gemma":[0.9994113,0.0002381298,0.00006448094,0.00006706735,0.0001982691,0.00002078014],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001684313,0.00006486488,0.001125383,0.0002435229,0.0000486075,0.0001027859,0.00007997973,0.002069554,0.5167467,0.0005847121,0.0007793248,0.4779862],"study_design_scores_gemma":[0.0001120452,0.0005716635,0.02514976,0.00008874957,0.0002424394,0.001971336,0.0001145127,0.3390199,0.6098082,0.001331291,0.02144933,0.0001407994],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03266282,0.0002866115,0.9654336,0.00004322843,0.000034069,0.00005592772,0.00008607502,0.0008828618,0.000514834],"genre_scores_gemma":[0.1037195,0.0004155467,0.8936963,0.00004719744,0.00005057585,0.00009415236,0.0002062015,0.0001018274,0.001668812],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00147695,"threshold_uncertainty_score":0.004712582,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02210699528726301,"score_gpt":0.308753698311977,"score_spread":0.286646703024714,"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."}}