{"id":"W4398561900","doi":"10.7910/dvn/lxvzrc","title":"Replication Data for: Application of Bayesian Additive Regression Tree to quantify the uncertainty of machine-learning derived variables: a case study in human activity patterns learned from accelerometer data","year":2023,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Replication (statistics); Computer science; Bayesian probability; Regression; Machine learning; Artificial intelligence; Regression analysis; Data mining; Tree (set theory); Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.006247328,0.001619525,0.0009837961,0.002019175,0.0007908228,0.001585934,0.003354152,0.002022286,0.009297175],"category_scores_gemma":[0.02566263,0.0005156268,0.001743219,0.002639944,0.0005542091,0.001356385,0.001658101,0.002302428,0.01205366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009881322,"about_ca_system_score_gemma":0.001433917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01719643,"about_ca_topic_score_gemma":0.03413666,"domain_scores_codex":[0.9961894,0.00113891,0.0004052231,0.0009327054,0.001129644,0.0002041838],"domain_scores_gemma":[0.9894361,0.002942329,0.0004012558,0.003979326,0.003052659,0.0001882134],"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":[0.001226009,0.0006970316,0.02939917,0.001260439,0.0005230089,0.0005977676,0.0003266333,0.03365855,0.002823412,0.00383059,0.8028715,0.1227859],"study_design_scores_gemma":[0.001581326,0.0007273728,0.05991155,0.0005363843,0.0002895779,0.001343743,0.0008404328,0.3229302,0.01766369,0.02836327,0.565394,0.000418469],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.06833324,0.001691033,0.1243698,0.002757632,0.001509829,0.001555497,0.753952,0.03349663,0.01233434],"genre_scores_gemma":[0.1264488,0.0003332759,0.1364193,0.0004935003,0.000184214,0.001966853,0.722978,0.00218307,0.008992972],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01719643,"threshold_uncertainty_score":0.03419262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1096443946345009,"score_gpt":0.363851420397077,"score_spread":0.254207025762576,"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."}}