{"id":"W2604730573","doi":"10.1038/srep45738","title":"Prediction of oxygen uptake dynamics by machine learning analysis of wearable sensors during activities of daily living","year":2017,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Cardiovascular and exercise physiology","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Institute for Aging; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Conselho Nacional de Desenvolvimento Científico e Tecnológico; AGE-WELL","keywords":"Wearable computer; Random forest; Activities of daily living; Computer science; Aerobic exercise; Dynamics (music); Machine learning; Simulation; Artificial intelligence; Physical therapy; Medicine; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001171363,0.00009807523,0.000719977,0.000356075,0.000243056,0.00002614784,0.00007905079,0.0000663455,0.00005546211],"category_scores_gemma":[0.0004014299,0.0000900188,0.0004935832,0.000313354,0.0003233724,0.0001344197,0.00009699762,0.0001157153,3.898323e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004179866,"about_ca_system_score_gemma":0.00005656768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005940241,"about_ca_topic_score_gemma":0.00005403921,"domain_scores_codex":[0.998458,0.00004380988,0.0005270228,0.0003642861,0.0004404035,0.0001664264],"domain_scores_gemma":[0.9978951,0.00002807233,0.0007890098,0.001056316,0.0001759773,0.00005557539],"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":[0.0001224596,0.000125588,0.1376565,0.0003541796,0.002041706,0.0000438499,0.001035676,0.009560681,0.847662,0.000002952935,0.000111271,0.001283128],"study_design_scores_gemma":[0.0002625478,0.00006773238,0.7685331,0.0003089058,0.002491224,0.00009893421,0.0008511473,0.03722483,0.1899134,0.00003461754,0.0001071625,0.0001064169],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976715,0.0003288015,0.000121482,0.00000866516,0.0006301182,0.0001157098,0.00002882381,0.00001747521,0.001077451],"genre_scores_gemma":[0.9966075,0.00006865053,0.0000813104,4.354754e-7,0.00001867316,0.000002747026,0.00009982041,0.00001085376,0.003109976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6577486,"threshold_uncertainty_score":0.367086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0114325241127672,"score_gpt":0.2352398650856672,"score_spread":0.2238073409728999,"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."}}