{"id":"W4232543982","doi":"10.22215/etd/2007-08158","title":"Unsupervised segmentation of heart sounds","year":2007,"lang":"en","type":"dissertation","venue":"","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Segmentation; Computer science; Beat (acoustics); Offset (computer science); Heart sounds; Autocorrelation; Artificial intelligence; Energy (signal processing); Sliding window protocol; Speech recognition; Computer vision; Estimator; Pattern recognition (psychology); Window (computing); Acoustics; Mathematics; Statistics","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":[0.0004304645,0.0006609096,0.000777226,0.002253356,0.0004093388,0.001322987,0.0007044698,0.0008577045,0.003215964],"category_scores_gemma":[0.001173291,0.0004027978,0.0008838502,0.00133767,0.0004585154,0.0007085463,0.0008205172,0.0007082327,0.003015851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003628674,"about_ca_system_score_gemma":0.0008450831,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002981434,"about_ca_topic_score_gemma":0.00612447,"domain_scores_codex":[0.9994527,0.00007599286,0.00003072217,0.0002274345,0.0001323708,0.00008076633],"domain_scores_gemma":[0.9994468,0.0001807723,0.00005172634,0.0001223873,0.000143266,0.00005513671],"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":[0.0003916984,0.0002150276,0.002735299,0.0001991932,0.000124547,0.0002011752,0.0002326267,0.01471471,0.2949289,0.00327809,0.01077434,0.6722043],"study_design_scores_gemma":[0.0001112219,0.0004352227,0.05140761,0.0001304255,0.0001801141,0.001049194,0.0003363642,0.7061224,0.1888424,0.01258501,0.03869752,0.0001024731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.104413,0.001734772,0.8829953,0.0002792678,0.0002780663,0.0001664783,0.001722903,0.003833711,0.0045765],"genre_scores_gemma":[0.3743068,0.001625054,0.582137,0.0002251392,0.0004709557,0.0002503484,0.01075016,0.001122645,0.02911189],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003215964,"threshold_uncertainty_score":0.01075846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01556540657197707,"score_gpt":0.3537052888860691,"score_spread":0.338139882314092,"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."}}