Salting Out: A Simple and Reliable Method to Distinguish Between Common Fluid Preservatives and Estimate Alcohol Concentration
Bibliographic record
Abstract
Abstract This paper details the salting-out method, which uses the salts potassium carbonate and sodium chloride to distinguish between the three most commonly used fluid preservatives: ethanol, isopropanol, and formalin. A summary of other methods to identify fluid preservative type and a review of the salting-out method published by Mayfield (2013, Distinguishing between ethanol and isopropanol in natural history collection fluid storage, Society for the Preservation of Natural History Collections, https://spnhc.org/wp-content/uploads/2018/11/Mayfieldfinalwithtablechanges.pdf ) are provided. A new salting-out method is presented, which requires a small fluid sample (2–4 ml). It is simple, quick, and relatively inexpensive to implement, making it a viable method to distinguish between common fluid preservatives. The materials and equipment for the salting-out test cost just over $100 US, and tests take approximately 3 minutes per container. Results of testing on known concentrations and combinations of ethanol, isopropanol, and formalin (a solution of formaldehyde in water) and on samples of fluid preservatives from specimen containers in the Smithsonian National Museum of Natural History and Bernice Pauahi Bishop Museum collections are presented. The results of salting-out tests have been verified by direct analysis in real time mass spectrometry (DART-MS) (Cody et al., 2005, Versatile new ion source for the analysis of materials in open air under ambient conditions, Analytical Chemistry 77(8):2297–302), which confirmed the results of salting-out tests but also highlighted some limitations, particularly when combinations of fluid preservative are encountered.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".