Arsenic and pre-1970s museum specimens: Using a hand-held XRF analyzer to determine the prevalence of arsenic at Naturalis Biodiversity Center
Bibliographic record
Abstract
Abstract The use of arsenic in the preservation of biological specimens was common practice prior to 1970. Because the Naturalis Center for Biodiversity (Naturalis) has extensive collections from before 1950, it was suspected that it held many contaminated specimens. In 2013, Naturalis tested 220 objects for the presence of arsenic over a period of 2 days using a handheld x-ray fluorescence analyzer, which detects arsenic, lead, mercury, and some other metals on objects. This testing provides an estimate of the prevalence of contaminated specimens, as well as a way to determine whether arsenic had spread into noncollection areas. In addition to specimens, floors, desks, keyboards, gloves, elevators, and lab coats were tested for arsenic presence and quantity. The results indicate that mounted specimens do not spread large amounts of arsenic onto the surrounding areas. However, there was sufficient contamination to warrant concern such that the arsenic-handling policy was modified to include different categories of contamination. From this framework, policy and physical changes to the building were made to minimize exposure by collections staff and visitors.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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 | 0.001 |
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".