{"id":"W3195526683","doi":"10.1109/lsens.2021.3107240","title":"Spectral Summation With Machine Learning Analysis of Tri-Axial Acceleration From Multiple Wearable Points on Human Body for Better Cough Detection","year":2021,"lang":"en","type":"article","venue":"IEEE Sensors Letters","topic":"Respiratory and Cough-Related Research","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Torso; Acceleration; Noise (video); Human stomach; Computer science; Mathematics; Medicine; Artificial intelligence; Physics; Stomach; Anatomy","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":[0.000239813,0.0001607325,0.0003783519,0.0003353766,0.0002103387,0.00004430537,0.00005099134,0.0001163906,0.0001454423],"category_scores_gemma":[0.0000957874,0.0001396239,0.0001948845,0.0005998702,0.00005084944,0.0001281748,0.000006453959,0.0004179493,0.0000116934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001039343,"about_ca_system_score_gemma":0.00003081894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002039946,"about_ca_topic_score_gemma":0.0002614566,"domain_scores_codex":[0.9984062,0.000202651,0.0003287917,0.0003688384,0.0004429368,0.0002505959],"domain_scores_gemma":[0.9991634,0.000201771,0.0001499553,0.0002459167,0.0001661901,0.00007274094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001397341,0.00011087,0.01454108,0.00003212782,0.001013091,0.00005630104,0.0002516169,0.02216455,0.9593483,0.000002434453,0.0001333025,0.0009489395],"study_design_scores_gemma":[0.003524768,0.0006322247,0.04664701,0.00008424803,0.0008926375,0.000004812161,0.00009541025,0.06066249,0.8867445,0.000008304036,0.000532674,0.0001708956],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.994275,0.00002122978,0.003571079,0.001512168,0.00009765175,0.0003671721,0.0000294184,0.00003941633,0.00008683736],"genre_scores_gemma":[0.9979394,0.00000788553,0.0003356609,0.0007207217,0.0002693487,0.00002025754,0.0003278514,0.00002863428,0.0003502074],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07260381,"threshold_uncertainty_score":0.5693699,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03671898721004192,"score_gpt":0.2990392247403516,"score_spread":0.2623202375303096,"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."}}