Preferred Chromaticity of Color-Tunable LED Lighting
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.
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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 | 0.000 | 0.001 |
| 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) | 0.003 | 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".