{"id":"W2333839922","doi":"10.3389/fnagi.2016.00049","title":"Working Memory Training and Speech in Noise Comprehension in Older Adults","year":2016,"lang":"en","type":"article","venue":"Frontiers in Aging Neuroscience","topic":"Hearing Loss and Rehabilitation","field":"Neuroscience","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Training (meteorology); Psychology; Working memory; Noise (video); Comprehension; Speech recognition; Audiology; Cognitive psychology; Computer science; Cognition; Medicine; Neuroscience; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005102504,0.0004330492,0.0003938723,0.0002404202,0.0002111325,0.0002099415,0.0001392331,0.000413441,0.001123599],"category_scores_gemma":[0.001451517,0.0001060724,0.0002044589,0.00008419042,0.0001831174,0.0002655327,0.0002396384,0.0002961013,0.0001606674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001018138,"about_ca_system_score_gemma":0.0001013958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008505535,"about_ca_topic_score_gemma":0.001291337,"domain_scores_codex":[0.9999,0.0000239642,0.00001451807,0.00002077221,0.00002436713,0.0000164626],"domain_scores_gemma":[0.9996229,0.00007977961,0.0001039773,0.00002697122,0.00004890606,0.0001173755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.02718058,0.02510274,0.5710548,0.0007887121,0.0006636187,0.001143863,0.008027884,0.0006350554,0.13511,0.0001240199,0.0008926296,0.2292762],"study_design_scores_gemma":[0.0002426659,0.02041453,0.9744775,0.00002670903,0.0001301363,0.0002532473,0.0003239481,0.0002594325,0.003359664,0.00009924054,0.0004028252,0.00001008827],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995033,0.0002354274,0.00003489339,0.00001292897,0.000003489074,0.000007917823,0.0000128372,0.00000251503,0.0001866776],"genre_scores_gemma":[0.9992927,0.0001631961,0.00009499011,0.0000291357,0.00001084883,0.00001530645,0.00003444732,8.039555e-7,0.0003584722],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001123599,"threshold_uncertainty_score":0.003758848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03330813356738044,"score_gpt":0.2642801853205599,"score_spread":0.2309720517531795,"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."}}