Bibliographic record
Abstract
Conventional textile screen printing processes have supported the fashion industry for centuries. With the advancement of digital technologies and the consumer purchase behavior shift to fast fashion and sustainable fashion methods, the industry’s printing needs are evolving, and manufacturers of digital textile printing devices are innovating to meet this demand. An overview of digital textile printing technologies will be given. As we re-emerge from the global pandemic of 2020, there is a significant growth prediction for the worldwide digital print textile market. Allied Market Research projects the value to quadruple to $8.8 billion by 2027. Research and Markets predicts a CAGR of 9.37% and estimates that the global digital textile printing market will reach $1.66 billion by 2026. The research paper will examine the technological advances in digital textile printing and the driving market forces influencing its growth momentum. Factors impacting the expansion in digital textile printing include agility and speed to market, cost-effective production processes, near-shore production, local material sourcing, increased creative application opportunities such as photorealistic reproduction quality and sustainable initiatives. Even conventional screen printers retrofit their equipment to support increased demand and speed in the printed textile market. With sustainability at the forefront of operations and corporate social responsibility, advancing digital textile printing methods point to more sustainable fashion production. A closer look at the environmental impact of digital textile printing methods will be explored to evaluate the promoted benefits.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.010 |
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".