Patterns on the Embroidered Textiles Unearthed from the Silk Road I: Animal Pattern
Bibliographic record
Abstract
Based on the archaeological evidences from the Silk Road, this paper reviews animal pattern on the embroidered textiles of the Han and Tang dynasties (2ndC BC- 9thC AD). The evidences show that animal pattern is widely found on the embroidered textiles unearthed from the graveyards or ancient sites along the Silk Road and particularly rich in variety. Generally, animals on the embroideries from the Silk Road can be categorized into animals of the real world and animals of the imaginary world. The first group consists of a range of real animals, including birds and butterflies which are usually flying in the sky or among the flowers, herbivorous animals like horses, antelopes, deer (especially reindeers) and yaks and carnivorous animals like tigers which are regular seen on the grasslands and aquatic animals like fish and turtles. The second group includes imaginary animals which play an important role in Chinese mythology like phoenixes, dragons and suanni etc. and significant legendary creature in Central Asian mythology like griffins. Besides, historical documents provide more information about animal pattern adopted by embroidered textiles than we have seen on archaeological evidences.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".