Natural History Wet Collections: Observations on PH Readings from the Use of Different Ethanol and Label Types
Bibliographic record
Abstract
Abstract We examined the effects of different types of specimen labels and tags on pH of different concentrations of ethanol typically used for fluid preservation in natural history collections. Labels were immersed in three different concentrations of ethanol, 96% pure undenatured ethanol (EtOH), 96% EtOH denatured with methyl-ethyl ketone (MEK), and 99.8% pure undenatured EtOH, with or without the presence of insect specimens, and the solutions were evaluated after 26 months for changes over time in pH reading. In general, pH readings of all label trials with 96% and 99.8% ethanol increased over time, except for trials of denatured alcohol, which demonstrated lower pH readings in almost all treatments, regardless of label type. Samples that contained labels with ordinary, nonstandardized, not explicitly acid-free printing paper had higher pH readings compared after the trial. Our observations are a good starting point for further experiments to answer research questions related to chemical interactions with labels in ethanol-preserved specimens, including tissue samples for molecular analyses, which can guide collection staff in their daily work.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".