Cleaning protocol for mercuric chloride–contaminated herbarium cabinets at the Smithsonian Museum Support Center
Bibliographic record
Abstract
Abstract Mercuric chloride has been used to control insect and fungal infestations in natural history collections for the past two centuries. Due to health concerns, its use was discontinued in the mid-1980s, but specimens treated with mercuric chloride are commonly found in modern collections and present a hazard to collection staff and researchers. Cabinets used to store mercuric chloride–treated specimens also become contaminated with the substance and represent a source of exposure even after specimens are removed. A team at the US National Herbarium, in coordination with the Smithsonian’s Office of Safety, Health and Environmental Management, developed a protocol to clean herbarium cabinets that were contaminated with mercuric chloride. Cabinets were cleaned with 70% ethanol and laboratory wipes, and effectiveness was measured using a portable mercury vapor analyzer and surface wipe sampling. Cleaning with ethanol was found to be more effective than just removing treated specimens, but the differences in reduction of airborne and surface mercury concentrations were not statistically significant. This study provides important insight and guidance for museums seeking to eliminate legacy mercuric chloride contamination from their herbarium cabinets.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.011 |
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".