Lighting quality research using rendered images of offices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it