Toxicity in 3D: XRF Analysis for the Presence of Heavy Metals in a Historical Stereograph Collection at Queen’s University Library, Canada
Bibliographic record
Abstract
This study employs non-destructive X-ray fluorescence (XRF) spectroscopy to identify the presence of potentially harmful heavy metals in a collection of nineteenth century stereographs housed at W.D. Jordan Rare Books and Special Collections at Queen’s University in Kingston, Ontario, Canada. Stereographs were extremely popular forms of entertainment and education in the Victorian era. As a result, they are common in archives, libraries, galleries, museums, and personal collections alike. This article provides an introduction to the history of stereographs, a background on their production, and pXRF analysis into the composition of pigments present on the stereograph mounts. Sixty-nine stereographs were selected for pXRF analysis, dating between 1852 and 1940, with the majority of the stereographs dating prior to 1895. Many of these cards are brightly coloured in greens, oranges, yellows, and pinks. Research revealed that arsenic-based pigment was common among all green stereograph cards analysed, lead-based pigment was common among all orange stereograph cards analysed, and lead- and chromium-based pigments were common among all the yellow cards analysed. However, additional analytical techniques need to be employed for definitive pigment identifications. This study demonstrates that hazardous pigments from the nineteenth century extend beyond wallpapers, books, and textiles and are likely to be pervasive in many heritage workplaces. This research highlights the importance of educating staff who work with archival collections. Understanding the scope of toxic pigments in archival collections is critical to ensuring proper handling, storage, and mitigation strategies to protect both the health of individuals and the integrity of these historically significant artifacts.
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 | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".