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Record W3048964982 · doi:10.1139/apnm-2019-0969

Examining the effects of applying ActiGraph low-frequency extension feature to analyze the sleeping behaviours of preschool-aged children

2020· article· en· W3048964982 on OpenAlexaffvenue
Hannah J. Coyle-Asbil, Becky Breau, David W.L., Jess Haines, Lori Ann Vallis

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2020
Typearticle
Languageen
FieldComputer Science
TopicContext-Aware Activity Recognition Systems
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsSleep (system call)NoveltySleep patternsAudiologyPolysomnographyAccelerometerPsychologyPhysical therapyPhysical medicine and rehabilitationMedicineCircadian rhythmComputer sciencePsychiatryElectroencephalographySocial psychology

Abstract

fetched live from OpenAlex

This study compares sleep outcome measures obtained using normal- and low-frequency extension (LFE) settings (Actilife). Forty-two children (aged 3–6 years) were instructed to wear an ActiGraph GT3X+ accelerometer on their hip for 7 days, 24 h/day. Total sleep time (min), sleep efficiency (%), and number and cumulative length (min) of awakening were used to compare the settings. Results suggest that the LFE setting results in significant but relatively small reductions in the sleep metrics of children. Trial registration no.: clincialtrials.gov (ID no. NCT02223234) Novelty LFE setting, available through ActiGraph, estimates a significantly reduced total sleep time and efficiency.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.908
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.231
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2020
Admission routes2
Has abstractyes

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