{"id":"W4233960846","doi":"10.21203/rs.3.rs-17367/v2","title":"Multichannel ECG Recording from Waist using Textile Sensors","year":2020,"lang":"en","type":"preprint","venue":"Research Square","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Waist; Textile; Environmental science; Computer science; Materials science; Medicine; Internal medicine; Composite material; Body mass index","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":[0.0002136501,0.0003757832,0.0003234044,0.000320455,0.0001109472,0.0003205157,0.0002932778,0.0005223229,0.0008633145],"category_scores_gemma":[0.0004117751,0.0001203336,0.0002885756,0.0003551801,0.0001197774,0.0003482809,0.0002535163,0.0001549444,0.0003105424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009118696,"about_ca_system_score_gemma":0.00005850773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002197279,"about_ca_topic_score_gemma":0.0003017771,"domain_scores_codex":[0.9997215,0.00004211495,0.00002518937,0.00008319053,0.0001042462,0.00002374349],"domain_scores_gemma":[0.9995926,0.0000960613,0.0001080759,0.00007001264,0.00009959755,0.00003365483],"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":[0.0002933718,0.00007750351,0.005733347,0.000283035,0.00003857593,0.0005974275,0.0001249424,0.0007712619,0.9497,0.00008431778,0.0004058747,0.04189035],"study_design_scores_gemma":[0.00006622751,0.002689571,0.1243268,0.00007740433,0.0001950817,0.004186796,0.0001936726,0.03165182,0.8313513,0.0001372339,0.005033019,0.00009103575],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9023737,0.001203807,0.09217331,0.0001847067,0.0002145747,0.00006951012,0.0004353611,0.000589925,0.002755143],"genre_scores_gemma":[0.9723756,0.0004584999,0.02551208,0.00007790644,0.00005615866,0.00002524598,0.0001381342,0.00002031305,0.001336018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008633145,"threshold_uncertainty_score":0.002888083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2434085923173799,"score_gpt":0.4697104447949548,"score_spread":0.2263018524775749,"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."}}