{"id":"W4312458426","doi":"10.1121/10.0015504","title":"The feces thesis: Using machine learning to detect diarrhea","year":2022,"lang":"en","type":"article","venue":"The Journal of the Acoustical Society of America","topic":"Phonocardiography and Auscultation Techniques","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Diarrhea; Excretion; Spectrogram; Feces; Defecation; Audiology; Outbreak; Microphone; Computer science; Event (particle physics); Medicine; Artificial intelligence; Acoustics; Biology; Pathology; Internal medicine; Microbiology; Telecommunications; Physics","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":[0.001071605,0.0005530951,0.0003977221,0.0005529973,0.0002876499,0.001208782,0.0005905548,0.0007349366,0.002914913],"category_scores_gemma":[0.003651278,0.0001662895,0.0004577482,0.0004656179,0.0004811126,0.0007981224,0.0005880928,0.001269462,0.0016645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004044564,"about_ca_system_score_gemma":0.000429428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009563855,"about_ca_topic_score_gemma":0.0005853681,"domain_scores_codex":[0.9995357,0.0001563001,0.00002207126,0.0001160384,0.0001240665,0.00004597619],"domain_scores_gemma":[0.9989055,0.0005781284,0.00007756858,0.00008350513,0.0002723727,0.00008292581],"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":[0.0004121988,0.000736469,0.01325376,0.0003092593,0.0002803145,0.0001728505,0.0002312786,0.05626494,0.01139453,0.01056073,0.07971535,0.8266683],"study_design_scores_gemma":[0.0001073483,0.000758754,0.01126923,0.0002525925,0.0000932404,0.0002800956,0.0002477133,0.8771046,0.02463107,0.02637524,0.05881395,0.00006610889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2571083,0.02065702,0.6039816,0.05186024,0.009817543,0.0005364146,0.001930122,0.002700843,0.05140788],"genre_scores_gemma":[0.699834,0.008502066,0.2157827,0.004372538,0.008686298,0.000330444,0.002456568,0.0003661723,0.05966923],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002914913,"threshold_uncertainty_score":0.00975132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01460730239695345,"score_gpt":0.2764797267690839,"score_spread":0.2618724243721304,"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."}}