Bibliographic record
Abstract
The presentation will focus on ‘Real Life’ machine installations and present the revolution from analogue to digital printing focusing on how specific industrial needs and met using the DReAM.One year ago at DPP2003 Reggiani Macchine presented our DReAM printer, this machine developed in collaboration with Scitex Vision and Ciba Specialty Chemicals introduced a novelty to the textile industry. The only digital printing machine designed for small production runs and high quality output: the DreAM.In fact our production speed is 150 m2 per hour with a printing resolution of 600 DPI.In 2004 at NIP20 we would like to present the evolution of our technology, essentially from inception to developmental prototype and focus on the industrial reality. Presenting under the session assignment of ‘Production Digital Printing’ we will illustrate the technological streamlining of our digital printing systems incorporating inline full cycle production situations developed specifically for some of our most avant-garde clients who together with Reggiani Macchine and our partners are clearly bridging the gap between analogue and digital realities in the printing industry. Integrating; preparation and finishing machinery, quality control and cutting instruments and the correct chemistry to ensure a high quality product at an industrial production level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.261 | 0.120 |
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".