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Record W2088235512 · doi:10.1210/jc.2004-2062

Blocking Low-Wavelength Light Prevents Nocturnal Melatonin Suppression with No Adverse Effect on Performance during Simulated Shift Work

2005· article· en· W2088235512 on OpenAlexaff
Leonid Kayumov, Robert F. Casper, Raed Hawa, Boris Perelman, Sharon A. Chung, Steven Sokalsky, Colin M. Shapiro

Bibliographic record

VenueThe Journal of Clinical Endocrinology & Metabolism · 2005
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsUniversity of TorontoMuscular Dystrophy CanadaUniversity Health Network
Fundersnot available
KeywordsMelatoninAlertnessNocturnalCircadian rhythmShift workEndocrinologyAudiologyInternal medicineMedicinePsychologyNeuroscience

Abstract

fetched live from OpenAlex

Decreases in melatonin production in human and animals are known to be caused by environmental lighting, especially short-wavelength lighting (between 470 and 525 nm). We investigated the novel hypothesis that the use of goggles with selective exclusion of all wavelengths less than 530 nm could prevent the suppression of melatonin in bright-light conditions during a simulated shift-work experiment. Salivary melatonin levels were measured under dim (<5 lux), bright (800 lux), and filtered (800 lux) light at hourly intervals between 2000 and 0800 h in 11 healthy young males and eight females (mean age, 24.7 +/- 4.6 yr). The measurements were performed during three nonconsecutive nights over a 2-wk period. Subjective sleepiness was measured by self-report scales, whereas objective performance was assessed with the Continuous Performance Test. All subjects demonstrated preserved melatonin levels in filtered light similar to their dim-light secretion profile. Unfiltered bright light drastically suppressed melatonin production. Normalization of endogenous melatonin production while wearing goggles did not impair measures of performance, subjective sleepiness, or alertness.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
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 score0.438
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.020
GPT teacher head0.303
Teacher spread0.283 · 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.

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

Citations137
Published2005
Admission routes1
Has abstractyes

Explore more

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