{"id":"W4398951009","doi":"10.7910/dvn/zs2z2j","title":"Replication Data for: Using machine learning methods to predict physical activity types with Apple Watch and Fitbit data using indirect calorimetry as the criterion.","year":2019,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Diet and metabolism studies","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Replication (statistics); Calorimetry; Computer science; Artificial intelligence; Mathematics; Statistics; Physics; Thermodynamics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00161196,0.0004237421,0.0009270828,0.0001223794,0.0003405569,0.0001233496,0.001083672,0.0001600909,0.0001177269],"category_scores_gemma":[0.002289413,0.0002795815,0.00004964294,0.0003035734,0.0001451413,0.0005848953,0.003658742,0.0006434087,0.0002494748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006165625,"about_ca_system_score_gemma":0.0002410511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001953919,"about_ca_topic_score_gemma":0.00008444943,"domain_scores_codex":[0.9972835,0.0002493339,0.0002403277,0.001432796,0.0004203052,0.0003737833],"domain_scores_gemma":[0.9922945,0.0004636706,0.0002874,0.006679976,0.0001182107,0.0001561836],"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":[0.000942536,0.0001700004,0.0003204562,0.0006181117,0.0006965728,0.00001089365,0.0001515939,0.00001632249,0.001539294,0.000002484934,0.9889678,0.006563985],"study_design_scores_gemma":[0.0007577171,0.0001658365,0.0002297931,0.0002421316,0.003151343,0.0000415897,0.0001288692,0.02269294,0.0002293205,0.000002356529,0.9720509,0.0003072171],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.007382006,0.00004799962,0.004452215,0.00006292151,0.0003518649,0.001488959,0.986156,0.00004254723,0.00001542718],"genre_scores_gemma":[0.000188788,0.0003429543,0.03416781,0.000202464,0.0007926847,0.00002603111,0.9640691,0.00005731056,0.0001528088],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0297156,"threshold_uncertainty_score":0.9999656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1217785048770845,"score_gpt":0.4083942818021967,"score_spread":0.2866157769251122,"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."}}