Bibliographic record
Abstract
In the 19th century, arsenic was a commonly used additive and colourant found in paper, clothing, household goods, personal products, and even confectionary items. Although most of these toxic products have long been removed from public consumption, books created using copper acetoarsenite, a green pigment, remain in our libraries and personal collections, with potential health implications. This article focuses on identifying 19th-century books in the Queen’s University Library, Kingston, suspected to contain copper acetoarsenite or emerald green. Based on visual identification, 150 books published between 1797 and 1900 were selected from the collections for X-ray fluorescence (XRF) spectroscopy testing to detect the arsenical colourant. Results revealed that 28 books tested contained significant amounts of arsenic in their bookcloth, covering paper, surface decoration, endpapers, or fore-edges. These findings underscore the necessity to implement proper handling and storage protocols and conservation strategies to mitigate the risk of arsenic exposure to library staff, researchers, and patrons. Moreover, this research contributes to the broader understanding of arsenic’s impact on cultural heritage preservation, highlighting the importance of interdisciplinary collaboration between librarians, conservators, archivists, historians, and scientists. By documenting and addressing arsenic contamination in library collections, institutions can safeguard the well-being of individuals interacting with these materials while preserving these cultural heritage items for the future.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.655 | 0.454 |
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".