Preferred Surface Luminances in Offices, by Evolution
Bibliographic record
Abstract
40 participants viewed a series of greyscale images of a typical non-daylit, open-plan partitioned office, and rated them for attractiveness. The image was projected onto a screen at realistic luminances and 54% of full size. The images in the series were geometrically identical, but the luminances of important surfaces were independently manipulated. Initially, the combinations of luminances were random, but as the session continued a genetic algorithm was used to generate new images that generally retained features of prior images that were rated most highly. As a result, the images presented converged on an individual's preferred combination of luminances. The results demonstrated that this technique was effective in reaching a participant's preferred combination of luminances. There were significant differences in room appearance ratings of the most attractive image compared to other images, and the differences were in the expected direction. Factor analysis of ratings of the most attractive images revealed a factor structure with some similarity to that obtained when people rated real office spaces. Furthermore, preferred luminances were similar to those chosen by people in real settings, as was the variation in preferences between individuals. Finally, subjective ratings of brightness, uniformity and attractiveness were significantly related to luminances in the image.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".