Arsenic and old quilts: Towards a better understanding of hazardous materials in the National Collection of Parks Canada through non-destructive XRF evaluation
Bibliographic record
Abstract
Many hazardous materials, such as arsenic and lead, are widely known to occur in collections of cultural heritage worldwide. However, analytical confirmation of these substances is less performed in the field of cultural heritage, at lest partially because of the reluctance of the stewards of these artifacts to remove samples from the objects for traditional chemical analysis. This is why non-destructive evaluation particularly x-ray fluorescence has become the standard for the positive identification of these substances in collections. This project using XRF thus enabled the testing of objects ranging from a taxidermy alligator, through historic quilts, shoes and pincushions to be assessed for hazardous chemical elements. Chromium, lead and mercury were occasionally detected in significant quantities on artifacts. However, it was the widespread detection of arsenic on various artifact types that was the most surprising result of this project. This is the topic of ongoing research, and several case studies, such as a quilt and a doll’s box, will be discussed more in depth. Despite these findings, a significant number of the tests revealed a negative result for inorganic contamination. The results of the testing were stored in CRMIS (Cultural Resource Information System), Parks Canada’s own adaptation of the widely used EMu software suite for museums. It was relatively easy to document positive results in this database. However, assuring that negative test results were also documented and easily searchable in the database needed development rule for data input. In conclusion, with targeted XRF analysis, interpretive research and good data management, a great deal of valuable information about hazards can be provided to end users of museum collections.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".