The effectiveness of light-emitting diode lighting for providing circadian stimulus in office spaces while minimizing energy use
Bibliographic record
Abstract
Architectural lighting has traditionally addressed visual performance and horizontal illuminance on the work plane, later focussing on energy efficiency, while only recently paying particular regard to human health outcomes. The present study evaluated the effectiveness of several light-emitting diode lighting strategies for delivering circadian stimulus to occupants of a typical office space while minimizing energy use. The study employed photometric simulations in a typical open-office space, delivering a criterion circadian stimulus of 0.3 to calculation points modelled at the simulated occupants’ eye level. Six luminaire types, two luminous intensity distributions, six spectral power distributions and two horizontal illuminances were evaluated, resulting in 144 unique lighting conditions. Additionally, the study calculated the discomfort glare for selected luminaires with the highest total lumen output, smallest aperture and direct-only luminous intensity distributions at the higher of the two horizontal illuminances (500 lx). The most impactful strategy involved supplementing common overhead lighting with a desktop luminaire delivering light directly to the simulated office occupants’ eyes, which provided greater circadian stimulus and used less energy than overhead luminaires that were capable of delivering the criterion circadian stimulus of 0.3.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".