Preferred surface illuminances and the benefits of individual lighting control : a pilot study
Bibliographic record
Abstract
Current office lighting recommendations stress the importance of vertical surface illumination over horizontal desktop illumination for VDT work, where the occupant is primarily in a 'heads up' position. We investigated this recommendation by creating two very different lighting conditions in two workstations in a mock-up open-plan office space. One workstation was provided with conventional, dimmable ceiling-recessed parabolic fixtures. The other, adjacent, workstation featured an innovative, dimmable, 'partition washer' system designed to preferentially light the vertical surfaces in the occupant's field of view; this was supplemented by a fixed 150 lx on the desktop from overhead. Participants (lighting experts) were assigned to one of the workstations and spent around 7 minutes reading and evaluating an on-screen article and a summary of the article, and completing an on-screen questionnaire on satisfaction with the lighting. This was done under one of four fixed initial lighting conditions. The participants then set the lighting to their own preference using on-screen dimmers, and repeated the task and questionnaire.Participants then switched workstations and repeated the procedure under the other lighting condition. Results showed that there was no significant difference in satisfaction between lighting conditions, although the partition washer condition required significantly less power. Participants were more satisfied with the lighting after control independent of lighting conditions, as expected. Further, derivation of preferred surface illuminances suggests that supplementary partition illuminance, beyond that which is provided by ceiling-recessed parabolics, is desirable.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".