{"id":"W3185776558","doi":"10.1123/jmpb.2019-0063","title":"Converting Raw Accelerometer Data to Activity Counts Using Open-Source Code: Implementing a MATLAB Code in Python and R, and Comparing the Results to ActiLife","year":2021,"lang":"en","type":"article","venue":"Journal for the Measurement of Physical Behaviour","topic":"Physical Activity and Health","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; University of Saskatchewan; Université de Montréal","funders":"","keywords":"Python (programming language); MATLAB; Accelerometer; Software; Computer science; Open source; Raw data; Algorithm; Source code; Simulation; Programming language; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002057136,0.0001229769,0.0004152553,0.00003951847,0.0003147108,0.0001248522,0.0002602148,0.0000243436,0.000003914568],"category_scores_gemma":[0.0003855957,0.00007803277,0.00004826008,0.000175066,0.00003378635,0.0002105841,0.00060365,0.000373759,9.486951e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001337889,"about_ca_system_score_gemma":0.000141064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001921367,"about_ca_topic_score_gemma":0.0002677575,"domain_scores_codex":[0.9985244,0.000101668,0.0003051701,0.000260957,0.0005174968,0.0002903251],"domain_scores_gemma":[0.9988793,0.0002465413,0.0001880546,0.0003351822,0.0001859555,0.0001650076],"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":[0.003547234,0.002240958,0.02529976,0.0003073643,0.0002813565,0.00001889107,0.003545444,0.0001618573,0.9196813,0.0001080327,0.001824151,0.04298368],"study_design_scores_gemma":[0.01605683,0.002189661,0.6304364,0.003639053,0.001787685,0.0002849211,0.003912392,0.1303209,0.186173,0.0004296669,0.02389867,0.0008708041],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949691,0.00003660797,0.0007975941,0.003375757,0.00007781484,0.0006212801,0.00004507581,0.000005469632,0.0000713297],"genre_scores_gemma":[0.9991216,0.00000794288,0.0003747946,0.0002747363,0.000160798,0.000008470429,0.000003465665,0.00001723013,0.00003096277],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7335083,"threshold_uncertainty_score":0.3182084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4688167538992314,"score_gpt":0.4611897263621136,"score_spread":0.007627027537117803,"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."}}