Flexographic Expanded gamut printing with Proprietary and Nonproprietary Characterization Charts
Bibliographic record
Abstract
Expanded gamut printing involves expanding the number of process colors by the three colors Orange, Green and Violet to create many spot colors with the new fixed CMYK-OGV ink set. 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. The authors conducted several studies with expanded gamut printing in digital printing, inkjet printing and offset printing. This study focuses on evaluating flexographic expanded gamut printing on a narrow web flexographic label press located at the School of Graphic Communications Management at RyersonUniversity, Toronto. Esko Equinox and GMG OpenColor expanded gamut software solutions were used, where each system was tested with its own proprietary characterization test chart. Idealliance ECG small v1 (2019) test target was also used in this study. A verification test chart was created, with selected Pantone spot colors. The verification test chart was processed using the characterization data from the proprietary and nonproprietary characterization press runs. The build of the selected Pantone colors was analyzed and CIEDE2000 was calculated. The main outcome from this study was that we conducted expanded gamut printing on a completely manual 7” narrow web label press and went through the step of optimization, curve calibration, characterization and verification. The average CIEDE2000 for the tested Pantone colors using the proprietary characterization charts was a CIEDE2000 of 2 and 3.2 with the Idealliance Small Chart. Both software solutions did better in regards to color accuracy with their proprietary characterization targets than using the data gathered from the Idealliance ECG small chart.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.019 | 0.003 |
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".