{"id":"W2098654070","doi":"10.1109/iembs.2006.260777","title":"Unsupervised and Uncued Segmentation of the Fundamental Heart Sounds in Phonocardiograms Using a Time-Scale Representation","year":2006,"lang":"en","type":"article","venue":"","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick; Defence Research and Development Canada","funders":"","keywords":"Phonocardiogram; Segmentation; Computer science; Morlet wavelet; Speech recognition; Artificial intelligence; Wavelet; Robustness (evolution); Pattern recognition (psychology); Representation (politics); Scale (ratio); Wavelet transform; Discrete wavelet transform","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":[0.0001607984,0.00008719863,0.0002023711,0.0001540152,0.00005119173,0.00001610764,0.00003082577,0.00005023533,0.00002605243],"category_scores_gemma":[0.000007219568,0.00006454634,0.0001400726,0.0006002877,0.000101924,0.0001187912,0.00002264563,0.00006451912,0.000001015101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000393901,"about_ca_system_score_gemma":0.0000220183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00114647,"about_ca_topic_score_gemma":0.00003155,"domain_scores_codex":[0.9992039,0.000069461,0.0002613872,0.0001650932,0.0001937979,0.0001064154],"domain_scores_gemma":[0.9996915,0.00002732673,0.00006124732,0.000148835,0.00004584826,0.00002517358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005804599,0.00008392827,0.483035,0.00002479427,0.00002426279,7.35049e-7,0.0002244194,0.00007041335,0.5151857,0.00001707721,0.0002647424,0.001010923],"study_design_scores_gemma":[0.001498377,0.00007139772,0.6296211,0.00007719027,0.00006990151,0.000029965,0.0006381036,0.001792597,0.3654526,0.0005937992,0.00006939642,0.00008550849],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960136,0.0001185214,0.001340941,0.0001502957,0.00002770049,0.0005098085,0.000005741955,0.00005000054,0.00178336],"genre_scores_gemma":[0.9921677,0.00001746035,0.007461456,0.0001216381,0.00002759784,0.00001792766,0.00003229477,0.000008873111,0.0001451248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1497331,"threshold_uncertainty_score":0.2632124,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01277322322106715,"score_gpt":0.2694160700694495,"score_spread":0.2566428468483823,"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."}}