Revisiting and Understanding the Removal of Mercuric Chloride Stains from Herbarium Sheet Labels: Updates and New Insights Since 1999
Bibliographic record
Abstract
Herbaria persistently battle insect infestations and mould growth, prompting the historical use of mercuric chloride as a pesticide. Although effective for decades, its unintended long-term consequences persist: notably dark stains that obscure critical label information, diminishing the scientific value of specimens, and lingering health and safety concerns when accessing collections. The stains remain a significant challenge to the National Herbarium specimens at the Canadian Museum of Nature’s Natural Heritage Campus, prompting conservators to revisit a 1999 method by Hawks and Bell for mercury stain removal using Lugol’s iodine solution. This study tested the 1999 method on heavily stained herbarium labels and compared a laboratory-prepared Lugol’s iodine solution with a commercial alternative. Both successfully removed mercury and its compounds, revealing previously obscured label information. Additionally, analysis using portable X-ray fluorescence (pXRF) and scanning electron microscopy with energy-dispersive spectroscopy (SEM-EDX) showed that removal was limited to the surface, leaving deeper contamination intact. While effective for improving label readability, the method does not eliminate the safety risks of handling mercury-contaminated sheets. This research aims to refine stain removal practices and offer a viable treatment option for herbaria with limited conservation resources.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".