Hazardous Hues: Identification of Arsenic Present in a Range of Colours Found on Historic Archival Material in the Collection of Parks Canada
Bibliographic record
Abstract
Since late 2019, Parks Canada has been active in the identification of hazardous materials in the collection under the care of the Indigenous Affairs and Cultural Heritage Directorate, using non-destructive XRF analysis. This method of analysis can detect elements of concern including lead, mercury, cadmium, and arsenic. In the case of arsenic, selected case studies show that arsenic is found in more places than initially expected. This paper outlines the XRF analysis of collections materials expected to be found in library and archives, and discusses the visual identification of arsenic, based on the colour of the material. Arsenic yellows (orpiment and/or realgar) were not positively identified in this survey, nor was cobalt violet (cobalt arsenate). A copper-arsenic green, likely emerald green, was occasionally detected. In addition, both a green ink distinct from typical arsenical greens, and dark reds were shown to contain varying levels of arsenic on paper artefacts during this survey. This paper posits the use of early synthetic organic pigments as an explanation for the presence of arsenic in the artefacts under investigation. Historical research indicates that aside from the colours green and yellow, arsenic can also be found in materials in the red and mauve colour families, from arsenic used in the synthesis of aniline dyes.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".