{"id":"W2729228834","doi":"10.2196/mhealth.7870","title":"Mobile Device Accuracy for Step Counting Across Age Groups","year":2017,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Physical Activity and Health","field":"Medicine","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; National Institute on Aging; University of Florida Health","keywords":"Wearable technology; Activity tracker; Wearable computer; Medicine; Physical medicine and rehabilitation; Body mass index; Activity monitor; Treadmill; Waist; Physical therapy; Physical activity; Gerontology; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0008001571,0.0002144228,0.0006763348,0.00003883996,0.00219865,0.0001265082,0.0001761117,0.0001564054,0.0000094398],"category_scores_gemma":[0.0002646106,0.0001855906,0.00009025141,0.00006403765,0.000168959,0.0003705866,0.00009544027,0.0004665705,0.00002063481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001463482,"about_ca_system_score_gemma":0.0005081958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008008468,"about_ca_topic_score_gemma":0.0004324614,"domain_scores_codex":[0.9977987,0.00004435371,0.0004174667,0.0004741396,0.0002521911,0.001013117],"domain_scores_gemma":[0.9976135,0.0003190861,0.0004778728,0.0006084064,0.0001150248,0.0008661427],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00472051,0.004009885,0.05805755,0.04935667,0.0001031132,0.00009147657,0.01327137,0.000001424446,0.0009211191,0.01213378,0.01475257,0.8425806],"study_design_scores_gemma":[0.009660596,0.005755859,0.7083529,0.0007191451,0.0001577679,0.0000822405,0.001500502,0.001775558,0.0001551854,0.0024118,0.268866,0.0005624463],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919204,0.0005390299,0.0003180307,0.003615317,0.0002560462,0.001997929,0.0001004418,0.00009118077,0.001161648],"genre_scores_gemma":[0.9918846,0.0009083002,0.001063976,0.004318265,0.001064439,0.0003380962,0.00004722585,0.00003528859,0.0003398234],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8420181,"threshold_uncertainty_score":0.9991003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1262270127844724,"score_gpt":0.4772073759178642,"score_spread":0.3509803631333918,"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."}}