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Record W6968009148 · doi:10.5281/zenodo.14603666

Bringing the Daylight Inside with Lighting Recipes

2023· article· en· W6968009148 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChemical and Physical Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsDaylightArtificial lightSunlightElectric lightCircadian rhythmEveningDaytime

Abstract

fetched live from OpenAlex

Our daily biological rhythm is synchronized to the 24-hour day on Earth, full of sunlight in the daytime and dark at night. Now that we are spending 90% of our time indoors, indoor lighting needs to adequately mimic sunlight to help maintain circadian rhythms, in addition to providing sufficient light for visual needs. Scientists have shown that light is a key signal for our body to function optimally and set our circadian clock to 24 hours to have a regular rhythm. Proper lighting indoors can help maintain this regular rhythm. Circadian lighting indoors should have the appropriate color temperature, spectral composition, brightness at the right time of day. There needs to be sufficient light stimulus during the daytime and a low level of light exposure in the evening and nighttime. Currently, the amount and type of light indoors in industrialized countries may be too low as reported by the International Commission on Illumination.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.023
GPT teacher head0.229
Teacher spread0.206 · 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 designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

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