The Effect of power constraints on occupant lighting choices and satisfaction: a pilot study
Bibliographic record
Abstract
Several recent studies have provided evidence that individual lighting control results in occupant satisfaction benefits. As part of a larger series of pilot studies, we examined the use of individual controls under various lighting power constraints. The studies took place in a mock-up open-plan office space, in which participants spent around two hours doing typical office tasks and answering satisfaction questionnaires. Participants were offered control over lighting circuits via virtual dimmers on their computer screen. The effect of power constraint varied depending on how the constraint was applied. When the constraint was obvious to the participant there were negative satisfaction effects, but when the constraint was hidden there were no apparent satisfaction penalties. One of the hidden constraints we applied was similar to a load shedding situation, whereby lighting is slowly dimmed to avoid peak load problems. We reduced light level slowly (1% of maximum output every 2 minutes) after participants had made their initial lighting level choices. Participants were not conscious of thedimming, allowing the illuminance to fall by 40-50% (typically) before choosing to increase light levels. Therefore, as a method of avoiding peak load-induced power outages this method has promise. These pilot study findings have important implications for practice, and warrant a larger future study.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".