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Record W2129392002 · doi:10.1093/sleep/30.6.797

Influence on Human Sleep Patterns of Lowering and Delaying the Minimum Core Body Temperature by Slow Changes in the Thermal Environment

2007· article· en· W2129392002 on OpenAlexaff
Fumiharu Togo, Seika Aizawa, Jun-ichiro Arai, Shoko Yoshikawa, Takayuki Ishiwata, Roy J. Shephard, Yukitoshi Aoyagi

Bibliographic record

VenueSLEEP · 2007
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCore temperatureSleep (system call)Core (optical fiber)ThermoregulationPsychologyMedicineEnvironmental scienceMaterials scienceInternal medicineComputer science

Abstract

fetched live from OpenAlex

STUDY OBJECTIVES: We hypothesized that appropriate changes in thermal environment would enhance the quality of sleep. DESIGN/SETTING: Controlled laboratory study. PARTICIPANTS: Healthy young men (n = 7, mean age 26 years). INTERVENTIONS: Nocturnal sleep structures in semi-nude subjects were compared between a condition where an ambient temperature (Ta) of 29.5 degree C was maintained throughout the night (constant Ta), and a second condition (dynamic Ta) where Ta changed slowly within the thermoneutral range (from 27.5 C to 29.5 degree C). MEASUREMENTS AND RESULTS: Statistically significant (P < 0.05) results included a lower and a later occurrence of minimum core body temperature (Tc), and a longer duration of slow-wave (stages 3+4) sleep in dynamic versus constant T. However, total sleep time, sleep efficiency, the total durations of light (stages 1+2) and rapid eye movement sleep, and the latencies to sleep onset, slow-wave sleep, and rapid eye movement sleep did not differ between conditions. CONCLUSIONS: Lowering the minimum and delaying the nadir of nocturnal Tc increases slow-wave sleep (probably by an increase of dry heat loss); use of this tactic might improve the overall quality of sleep.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.389

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.271
Teacher spread0.258 · 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 designObservational
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

Citations52
Published2007
Admission routes1
Has abstractyes

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