Long-term visual quality evaluations correlate with climate-based daylighting metrics in tropical offices – A field study
Bibliographic record
Abstract
In this paper, we present a post-occupancy study of 326 participants in 10 daylit office buildings in Singapore and correlate the results with climate-based daylighting metrics and electric lighting simulations using calibrated simulation models of the 10 buildings. For the first time, this study tests the climate-based daylighting metrics which are used in building design against their impact on occupant perception within buildings. We find significant correlations between climate-based daylighting metrics and reported occupant satisfaction with access to daylight, view interest, perception of ‘too low’ lighting levels and visual comfort. Overall, climate-based daylighting metrics which account for lower illuminance thresholds such as continuous daylight autonomy and useful daylight illuminance combined (100–3000 lx) correlate more strongly with subjective results than do electric lighting sufficiency metrics such as daylight autonomy at 300 and 500 lx thresholds. Simple descriptive statistical representations of annual daylight distributions, mean and median annual daylight illuminance values, outperform climate-based daylighting metrics in correlation strength and p-value. Based upon these results, new metrics are proposed for occupant satisfaction with daylight access and views. In addition, increased daylight levels are shown to decrease reporting of lighting levels being ‘often too low’ even when adequate electric lighting is provided, and contrast is likely to be beneficial to space perception at non-glaring thresholds.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".