Revisiting and Understanding the Removal of Mercuric Chloride Stains from Herbarium Sheet Labels: Updates and New Insights Since 1999
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it