A Monte Carlo method for assessing color rendering quality with possible application to color rendering standards
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
Abstract The lighting industry has been increasingly challenged to reduce electrical energy consumption while providing illumination with sufficient color rendering quality. As a result, the problem of accurately assessing color rendering quality has gained increased prominence and the introduction of efficient narrow band light emitting diode (LED) sources has further intensified the debate. This study argues that there is a basic problem with the traditional method of quantifying color quality color rendering index (CRI), one that cannot be solved through minor improvements. The CRI relies on a determination of the degree of color distortion that a test source produces for a small number of test samples of specified spectral reflectance distribution, but there is no clear objective rationale for selecting these few samples. Also, any such arbitrary scoring scheme lacks an objective argument for what constitutes an acceptable score. This study proposes a new method for color rendering assessment that determines the color shift of one thousand, or more, representative reflection spectra that span the full multidimensional range of possible spectral distributions and colors. This broad sampling eliminates the intrinsic selection bias of the CRI calculation and its variants and it is compatible with a more objective standard for a color quality score, one that is statistically based on the fraction of the test spectra that experience color shifts that are less than a just noticeable difference (JND), or an agreed upon multiple of it. Since the concept of JNDs in color has been reproducibly quantified, it is hoped that this approach will be widely acceptable. © 2010 Wiley Periodicals, Inc. Col Res Appl, 2012
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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