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Record W2336599738

Principles of healthy lighting: highlights of CIE TC 6-11's forthcoming report

2002· article· en· W2336599738 on OpenAlexaffvenue
Jennifer A. Veitch

Bibliographic record

VenueNPARC · 2002
Typearticle
Languageen
FieldNeuroscience
TopicCircadian rhythm and melatonin
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsElectric lightVisibilityMoodQuality (philosophy)PsychologyArtificial lightArchitectural engineeringComputer scienceEngineeringGeographySocial psychology
DOInot available

Abstract

fetched live from OpenAlex

In the late 1990s, CIE began to shift its emphasis from lighting for visibility to a more broad definition of lighting quality, encompassing human needs, architectural integration, and economic constraints (including energy) [41]. Human needs, as defined here, include lighting that is appropriate to maintain good health, as well as lighting for visibility, task performance, interpersonal communication, and aesthetic appreciation.Among other developments, this definition reflects the many demonstrations that there are nonvisual, systemic effects of light in humans. Specifically, controlled laboratory and clinical studies have demonstrated that light processed through the eye can influence human physiology, mood and behaviour. These findings may provide the basis for major changes in future architectural lighting strategies.The report of CIE TC 6-11 summarises the literature in this rapidly-developing area through December 2001, including the neurophysiology, neuroanatomy, behavioural effects of daytime and night-time effects of light exposure in healthy people, and therapeutic effects of light. The report concludes with preliminary guidance concerning healthy lighting and how it might be applied architecturally. This presentation will focus on the possible architectural applications of this area of research, for lighting interiors occupied by day and by night, as a step towards lighting recommendations that respect the broadest definition of lighting quality.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score0.420

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 head0.064
GPT teacher head0.271
Teacher spread0.207 · 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

Citations3
Published2002
Admission routes2
Has abstractyes

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