Color Accuracy of Corporate Colors in Expanded Gamut Print Reproduction
Bibliographic record
Abstract
Expanded gamut printing, as the name implies, expands the gamut of printable colors that can be achieved through a combination of CMYK by adding Orange, Green and Violet (OGV). Through the addition of these additional three colors it is possible to cover almost 95% of the colors that are in the Pantone® color guide for printing. The benefit of using expanded gamut printing is, that the same inks can stay in the ink fountains of a printing press and only the printing plates need to be changed from job to job. No time-consuming ink changes are necessary if a printing press uses the expanded gamut ink set. The exact reproduction of brand identity colors is very important to brand owners. Brand owners allow minimal color discrepancies of their brand color(s). Therefore, it is important to achieve the best possible color reproduction when printing brand colors using the expanded gamut technology. It is the goal of this project to find out how much color deviation there is when a Pantone® color is reproduced with expanded gamut technology. In this project 14 brand colors that use a Pantone® color were investigated. These brand colors should cover all aspects of the color wheel. The first part of the project was the characterization of the press followed by a press run of the 14 selected brand colors. The accuracy of the color reproduction was checked against the digital color values for the selected Pantone® colors from the Pantone Expanded Gamut guide. From the 14 selected colors 10 colors showed a more accurate color reproduction. The average DE2000 value for the tested colors was 3.24 vs. 6.24 for the four-color version. The main challenges in this project were running a seven-color job on a four-color press and controlling the ink/water balance for the Orange, Green and Violet inks on press.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".