{"id":"W7077489908","doi":"10.26092/elib/4403","title":"Machine Learning Approaches to Predicting Energy Expenditure in Preschool Children: Insights from Accelerometry, Gyroscope Data, and Cross-National Validation","year":2025,"lang":"en","type":"article","venue":"Media (https://www.suub.uni-bremen.de/)","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wearable computer; Accelerometer; Preprocessor; Energy expenditure; Measure (data warehouse); Calibration; Process (computing); Feature (linguistics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01144287,0.001614268,0.001147833,0.00213446,0.000498181,0.001169433,0.001559441,0.001269686,0.000542679],"category_scores_gemma":[0.01703572,0.0004191717,0.001374231,0.001459493,0.0006207884,0.000944413,0.001245727,0.002172173,0.0002013637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00140641,"about_ca_system_score_gemma":0.001762668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.037191,"about_ca_topic_score_gemma":0.02898867,"domain_scores_codex":[0.9975734,0.001361996,0.0002325684,0.0004094731,0.0002853483,0.000137154],"domain_scores_gemma":[0.9894716,0.008385028,0.0004651418,0.0005709183,0.0009633006,0.0001439738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0005275403,0.0006936126,0.2157792,0.0003191502,0.001277977,0.0003466133,0.000716833,0.5975151,0.001380547,0.002608125,0.002029554,0.1768058],"study_design_scores_gemma":[0.00002187988,0.0001709109,0.03523549,0.0001250505,0.0001004352,0.00005139548,0.0002852837,0.9602693,0.0007957654,0.00232445,0.0005963492,0.00002365603],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8216679,0.007229071,0.1670352,0.0007226933,0.00008780765,0.0001752692,0.0007408167,0.0005339303,0.001807423],"genre_scores_gemma":[0.9529797,0.0009912553,0.04313499,0.00009563618,0.00003011882,0.0001250529,0.002055306,0.00003984334,0.0005480286],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.037191,"threshold_uncertainty_score":0.07394904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04973856456447864,"score_gpt":0.2631393645028363,"score_spread":0.2134007999383576,"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."}}