Sustainability benefits of expanded gamut printing
Bibliographic record
Abstract
Expanded gamut printing is a relatively new technology that allows print companies to move away from spot colors and require an inventory of them on hand or order them from their ink supplier whenever a print job requires a spot color. With expanded gamut printing, the same seven colors remain in the printing units, and only the printing plates and the substrate get changed. This leads to less frequent ink changes and print unit wash-ups and allows the so-called ganging of jobs, resulting in less use of organic solvents and lower paper consumption. Also, the target printing ink densities for the seven colors were established during the characterization press runs. In turn, the press operator knows which target densities must be achieved to obtain optimum color balance, resulting in shorter make-ready times. The savings mentioned above make the operation of a print company more sustainable. A list of currently used Pantone colors will be compiled and converted to their expanded gamut version in cooperation with a local print company. The color difference to the digital Pantone library will be determined so brand owners know there might be a color difference. This study focuses on the sustainability aspects of expanded gamut printing. The main goal of this project is to quantify these savings so that expanded gamut printing will achieve broader acceptance in the industry based on its advantages and enhanced sustainability of the print operation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".