Specimen whitening: An assessment of methods of ammonium chloride smoke removal
Bibliographic record
Abstract
Abstract Smoking is the process of subliming and depositing ammonium chloride or other white powder onto specimens, and is useful for enhancing specimen relief for photography. Ammonium chloride is acidic and highly soluble in water, and can etch delicate specimens in the presence of moisture. Though many methods exist for applying ammonium chloride to specimens, removing the coating is rarely discussed. To amend this, we performed an experiment smoking a series of invertebrate fossil specimens and cleaned them using eight different cleaning techniques. After undergoing the appropriate cleaning method, each specimen was then thoroughly rinsed in deionized water. Using a silver nitrate solution, which precipitates silver chloride in the presence of chloride ions, we tested the rinse water for remaining chloride contamination. Using this procedure, we found complete rinsing of the specimen to be the only method for removing contamination to a point below our detection limit, although various brushing techniques were moderately effective. Breathing on the specimen, a commonly used method, was ineffective, and likely exacerbates the problem of etching by dissolving remaining residue. We recommend a case-by-case approach to ammonium chloride residue removal, using one or more techniques, while making sure to record the smoking and cleaning procedures in your collection’s database.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".