{"id":"W6977897428","doi":"10.7910/dvn/kafexz","title":"HLS-CMDS: Heart and Lung Sounds Dataset Recorded from a Clinical Manikin using Digital Stethoscope","year":2025,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Stethoscope; Heart sounds; Sound (geography); Lung; Auscultation; Crackles","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":[0.0005907687,0.001865879,0.001073363,0.001786034,0.0004763959,0.0009420808,0.001909071,0.001901843,0.01181813],"category_scores_gemma":[0.002651084,0.0002584259,0.0009220848,0.001901932,0.0003140754,0.000678809,0.001521674,0.000974579,0.02187375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009025512,"about_ca_system_score_gemma":0.001081297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01289916,"about_ca_topic_score_gemma":0.02771625,"domain_scores_codex":[0.999202,0.0001349512,0.0001573672,0.0001802133,0.0002116687,0.0001138386],"domain_scores_gemma":[0.9989138,0.0002223698,0.00008815785,0.0002265462,0.0004113347,0.0001379091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006279806,0.0002885268,0.01059217,0.001974223,0.0001407516,0.0003692016,0.0001163112,0.001268898,0.002013506,0.0003851827,0.9445615,0.03766166],"study_design_scores_gemma":[0.0007586115,0.0005589459,0.1469223,0.00125998,0.0002130251,0.001833514,0.001352427,0.01068485,0.006959183,0.002071591,0.8270891,0.0002964549],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.007470815,0.0004626007,0.0007815053,0.0002386955,0.0001684473,0.0001201712,0.9877229,0.00105548,0.001979331],"genre_scores_gemma":[0.007310673,0.0001619457,0.0009697679,0.00008882095,0.00003073509,0.0002347987,0.9900052,0.00003270938,0.001165416],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01289916,"threshold_uncertainty_score":0.03953564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02267557433445818,"score_gpt":0.2882941360905103,"score_spread":0.2656185617560521,"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."}}