Preferred Chromaticity of Color-Tunable LED Lighting
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
Previous research has demonstrated that individual personal control over light level benefits individuals and organizations. As a first step toward testing whether light source spectrum choices—which are possible with light emitting diode (LED) systems—offer similar benefits, we examined preferences for various spectra in a scale model of an office. Participants judged the model’s brightness, colorfulness, and pleasantness when lit with five preset spectra with measured correlated color temperatures (CCTs) of 2855, 3728, 4751, 5769, and 6507 K created with five LED channels and one fluorescent spectrum (3750 K measured), all at approximately 500 lx. Then they chose their preferred light spectrum using the five LED channels, once as a free choice and once with an illuminance limit. Judgments of the fluorescent spectrum and the LED spectrum with the closest (matched) CCT did not differ. The preset judgments followed a quadratic pattern, with the lowest and highest CCT conditions having lower ratings than the three middle conditions. The free and illuminance-constrained lighting choices did not differ, with individuals’ selections ranging from 2850 to 14,000 K and generally lying slightly below the blackbody curve.
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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