Ink marks, bronze crossbows and their implications for the Qin Terracotta Army
Bibliographic record
Abstract
Abstract At the heart of bureaucratic practice during Warring States and early Imperial China were regular, small acts of accountancy in which objects and people were marked so that their movements could be kept track of, their quality checked and their numbers marshalled. In the mausoleum complex of the Qin Shihuang (259-210 bc, the First Emperor of the Qin Dynasty), the longer texts and shorter inscribed marks found on the bronze weapons of the Terracotta Army are reasonably well known, and such information helps us to understand aspects of Qin craft organisation and logistics at this crucial period of Chinese state formation. This paper’s modest starting point is a study of two further, less well-known ink inscriptions found on crossbow triggers from Terracotta Army Pit 1. Using multispectral photography, digital microscopy and Raman analysis, we uncover evidence of further marks on the same two triggers that suggest a similar pattern of ‘matching’ marks as suggested by the incised evidence. We also identify the black substance used to make the marks as a soot-based ink. Spatial analysis of both the inked and incised trigger marks then provides wider context for how such marking practices amongst Qin bronze-workers may have operated.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".