Lighting quality research using rendered images of offices
Bibliographic record
Abstract
Forty participants viewed a series of high-quality, computer-rendered colour images of a typical open-plan partitioned office, and rated them for attractiveness. The images were projected at realistic luminances and 33% of full size. The images were geometrically identical, but the outputs of four lighting circuits depicted in the renderings were independently manipulated. Initially, the lighting circuit outputs were random, but a genetic algorithm was used to generate new images that retained features of prior, highly-rated, images. As a result, the images converged on an individual’s preferred scene. Luminances in the preferred image were similar to preferred luminances chosen by people in real settings. A sub-set of images was rated on Brightness, Non-Uniformity and Attraction scales. Ratings were significantly related to simple photometric descriptors of the images. In particular, around 50% of the variance in Attraction ratings was predicted by average image luminance and its square, or by average image luminance and a measure of luminance variability.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".