A Monte Carlo method for assessing color rendering quality with possible application to color rendering standards
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
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.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".