{"id":"W2084550115","doi":"10.1016/j.eswa.2013.03.028","title":"A heterogeneous framework for real-time decoding of bioacoustic signals: Applications to assistive interfaces and prosthesis control","year":2013,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Engineering and Physical Sciences Research Council","keywords":"Computer science; Task (project management); Interface (matter); Bioacoustics; Decoding methods; Interference (communication); Artificial intelligence; Support vector machine; Human–computer interaction; Speech recognition; Channel (broadcasting); Engineering","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":[0.0001170647,0.0002115118,0.0003525199,0.0001286048,0.0002514656,0.0001619943,0.0003406503,0.00008375882,0.00001579337],"category_scores_gemma":[0.00008428831,0.0001639169,0.00004432536,0.0002945357,0.0001440391,0.00011094,0.00004908099,0.00006857066,0.00006377033],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004045918,"about_ca_system_score_gemma":0.00003207446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001003556,"about_ca_topic_score_gemma":0.000002714566,"domain_scores_codex":[0.9984791,0.00007254027,0.0004082232,0.0005898525,0.0001737808,0.000276558],"domain_scores_gemma":[0.9974672,0.001490133,0.0002494551,0.0004384408,0.0001878837,0.0001668644],"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":[0.00005863709,0.0001941363,0.0001581224,0.0001625161,0.00006029049,4.332184e-7,0.001226322,0.001416097,0.9847748,0.004769977,0.0008347339,0.006343937],"study_design_scores_gemma":[0.001529322,0.001481671,0.0003664446,0.001139002,0.0001410092,0.0001727957,0.002305049,0.09032499,0.8811454,0.00253497,0.01746657,0.001392745],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05535182,0.0002881009,0.9360098,0.0005592344,0.00003284502,0.007254462,0.0001416664,0.0001486901,0.0002133766],"genre_scores_gemma":[0.9474135,0.00001739807,0.02410934,0.0002337495,0.0001118594,0.02791883,0.000003055342,0.0000344372,0.000157832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9119005,"threshold_uncertainty_score":0.6684337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02302701019153711,"score_gpt":0.291390124356127,"score_spread":0.2683631141645899,"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."}}