Chromaticity‐matched but spectrally different light source effects on simple and complex color judgments
Bibliographic record
Abstract
Abstract As light‐emitting diode (LED) light sources mature, lighting designers will be able to deliver white light with a variety of spectral power distributions and a variety of color rendering properties. This experiment examined the effects of three spectral power distributions (SPDs) that were matched in illuminance and chromaticity on three measures of color perception: one objective (performance on the Farnsworth‐Munsell 100 hue test) and two subjective (judgments of the attractiveness of one's own skin, and preferences for the saturation of printed images). The three SPDs were a quartz‐halogen (QH) lamp and two LED sources that were matched to the QH lamp in terms of both illuminance and chromaticity; the three light sources were nominally CCT = 3500 K, x = 0.40, y = 0.39 and ∼ 400 lx. LED A used three channels (red, green, blue), and had very poor color rendering (Ra = 18). LED B used four channels (red, amber, cyan, white) and had very good color rendering (Ra = 96, whereas the QH had Ra = 98). Secondary hypotheses addressed the effects of age and skin and eye color on the dependent measures. As expected, LED A delivered very different color perceptions on all measures when compared to QH; LED B did not differ from QH. The results show that it is possible for LED sources to match the familiar incandescent sources. However, although it is possible to deliver what appear to be millions of colors with a three‐chip (RGB) device, there is the risk of creating a very poor luminous environment. © 2013 National Research Council Canada and Wiley Periodicals, Inc. Col Res Appl, 39, 263–274, 2014; Published Online 12 April 2013 in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/col.21811
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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 | 0.007 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".