Evaluating the <scp>ISO</scp> Standards for <scp>UV</scp> ‐Content Adjustment Based on Brightness and Whiteness
Bibliographic record
Abstract
ABSTRACT Whiteness and brightness are essential perceptual attributes of paper and packaging, significantly influenced by the UV content of illumination, especially when fluorescent whitening agents (FWAs) or optical brightening agents (OBAs) are present. This study quantitatively evaluates the performance of four ISO standards regarding UV‐content adjustment towards two standardized illuminations: ISO 2470‐1 and ISO 11476 for C, and ISO 2470‐2 and ISO 11475 for D 65 . The study involves two fluorescent IR2 (international reference of level 2) standards, whose total spectral radiant (TSR) factors were measured at National Research Council Canada (NRC). The results indicate that all standardized methods effectively reproduce the assigned TSR factors, despite discrepancies in several fluorescence‐intensive wavelengths. However, systematic differences between the ISO standards suggest non‐interchangeability: UV content adjusted with ISO Brightness is slightly higher than with CIE Whiteness; spectrophotometers adjusted against CIE Whiteness read lower ISO Brightness, and vice versa. These differences are more pronounced for C than D 65 and decrease with lower FWAs content. Given the spectral dependence of assigned values—entire spectrum for CIE Whiteness and blue spectral band for ISO Brightness—ISO Brightness‐based methods (ISO 2470‐1 for C and ISO 2470‐2 for D 65 ) are more straightforward for UV adjustment.
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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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".