{"id":"W2157275022","doi":"10.1109/iembs.2007.4353003","title":"Feature selection for swallowing sounds classification","year":2007,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Dysphagia Assessment and Management","field":"Health Professions","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Swallowing; Mahalanobis distance; Computer science; Feature selection; Feature extraction; Feature (linguistics); Pattern recognition (psychology); Set (abstract data type); Speech recognition; SIGNAL (programming language); Artificial intelligence; 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":[0.001397006,0.001078143,0.001490418,0.002604875,0.0004731769,0.0008498924,0.0005953145,0.0006474748,0.002270155],"category_scores_gemma":[0.004648546,0.0002179859,0.00102737,0.002185977,0.0002474796,0.0005749138,0.0004353815,0.0005456764,0.0009275498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002547275,"about_ca_system_score_gemma":0.0005488241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002121332,"about_ca_topic_score_gemma":0.001105171,"domain_scores_codex":[0.9991919,0.0002745162,0.00009262488,0.000141761,0.0002186135,0.00008050429],"domain_scores_gemma":[0.9983568,0.0009401362,0.0000912389,0.0001036012,0.0004672036,0.00004090392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004852766,0.0001633627,0.004799135,0.0002418352,0.000132021,0.0001794722,0.00009349674,0.01448189,0.0287573,0.0006422996,0.003288693,0.9467351],"study_design_scores_gemma":[0.0002288,0.001443878,0.05580577,0.0002049399,0.0005903484,0.0009650467,0.0005527614,0.8412477,0.07043377,0.006106637,0.02225776,0.0001624795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1841563,0.003529857,0.8037087,0.000416682,0.0002172228,0.0004561018,0.00173139,0.002932414,0.002851235],"genre_scores_gemma":[0.6318006,0.001160982,0.3594489,0.00009874548,0.0002026501,0.0006994884,0.00430627,0.0001551345,0.002127226],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002604875,"threshold_uncertainty_score":0.007594466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09504688628664869,"score_gpt":0.4289760956468979,"score_spread":0.3339292093602492,"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."}}