A New method of deriving illuminance recommendations for VDT offices
Bibliographic record
Abstract
This paper suggests a new method of objectively deriving illuminance recommendations for VDT offices. An experiment in a mock-up office space gave occupants control over dimmable lighting circuits after a day working under constant, pseudo-random lighting conditions (for example, desktop illuminance during the day varied between 100 to 700 lux). Data analysis indicated that the lighting experienced during the day influenced the changes in lighting made at the end of the day. Many occupants chose to reduce screen glare if any existed. Even after statistically removing the effect of glare, those exposed to lower illuminances during the day chose higher illuminances at the end of the day, and those exposed to higher illuminances during the day chose lower illuminances. Regression of these end-of-day preferences relative to the illuminance experienced during the day can yield a preferred illuminance, equivalent to the daytime illuminance at which no change was preferred at day's end. Using this method, preferred illuminances in the range 200 to 500 lx were derived. Further, the deviation between participants' lighting preferences and the lighting they experienced during the day was a significant predictor of participant mood and satisfaction.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".