Development of Digital Quasi-embossing Technology with an Inkjet Printer-2
Bibliographic record
Abstract
Conventional embossing printing technology which can be used to produce metallic glossy images on stereo-shaped objects needs a pressing plate with the image patterns specially arranged. Accordingly, the embossing system is relatively expensive and lacks the adaptability for high-mix low-volume production. Considering these facts, we developed the Digital Quasiembossing Technology (DQT) which consisted of the technology combined with UV-curable inkjet printing and hot foil-stamping. We reported the DQT for the first time at NIP 28 in Quebec City last year. This paper is a continuation of the last report and describes the following facts. (a) We have improved the adhesion of UV-curable ink to a metallic foil, and consequently we can make not only relatively small-sized products but also large-sized ones with pictures and letters printed thereon. (b) The improved DQT system can produce higher gloss than the last system reported at NIP 28.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".