Cleaning protocol for mercuric chloride–contaminated herbarium cabinets at the Smithsonian Museum Support Center
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
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.
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 | 0.001 | 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) | 0.002 | 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 it