{"id":"W1968150725","doi":"10.1016/j.chaos.2007.01.071","title":"Swallowing sound detection using hidden markov modeling of recurrence plot features","year":2007,"lang":"en","type":"article","venue":"Chaos Solitons & Fractals","topic":"Dysphagia Assessment and Management","field":"Health Professions","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Swallowing; Hidden Markov model; Computer science; Sound (geography); Spectrogram; Speech recognition; Viterbi algorithm; SIGNAL (programming language); Pattern recognition (psychology); Audio signal; Recurrence plot; Artificial intelligence; Acoustics; Medicine; Speech coding; Nonlinear system","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":[0.001692598,0.0002358916,0.0003745355,0.0002959944,0.0006939191,0.0000215883,0.0002467546,0.0002465151,0.0001466],"category_scores_gemma":[0.00020135,0.000230884,0.0001287705,0.0003334494,0.00004906744,0.0003823654,0.0002227541,0.0005518128,0.00003419117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003119268,"about_ca_system_score_gemma":0.0001367027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005626241,"about_ca_topic_score_gemma":0.0003989851,"domain_scores_codex":[0.9974665,0.0001794188,0.0007758475,0.00037218,0.0004230976,0.0007829388],"domain_scores_gemma":[0.9984744,0.0003819028,0.000383294,0.000409713,0.0001979682,0.0001527176],"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":[0.002511415,0.002373737,0.2447659,0.007304412,0.00278606,0.0002646589,0.06658389,0.0070519,0.3285436,0.01571863,0.006331021,0.3157647],"study_design_scores_gemma":[0.01428927,0.0013752,0.1698367,0.01044227,0.002887259,0.00005884413,0.1805716,0.484338,0.05168759,0.05492151,0.02254712,0.007044629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9030782,0.0003669077,0.07948245,0.0001129797,0.00190374,0.0008601465,0.000007867273,0.0001387825,0.01404894],"genre_scores_gemma":[0.9939849,0.00004885652,0.004362245,0.0002127864,0.0005300319,0.00003556212,0.00001376651,0.00004476434,0.0007671203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4772861,"threshold_uncertainty_score":0.9415175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08065918999847904,"score_gpt":0.4284169385446137,"score_spread":0.3477577485461346,"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."}}