STORAGE FACTORS INFLUENCING ETHANOL CONCENTRATION OF FLUID-PRESERVED INSECTS
Bibliographic record
Abstract
Abstract The scientific value of insect specimens stored in ethanol depends upon their adequate preservation, which is most directly impacted by the preservative solution’s concentration. Determining what storage factors influence ethanol concentration, including the size and type of container and closure, as well as the ratio of specimen to ethanol volume, can inform collections staff on how to manage ethanol-preserved insect specimens ideally. We hypothesize that ethanol solution concentrations would 1) decrease with increasing insect volume to ethanol volume ratios, 2) decrease with time, 3) vary by cap liner material, and 4) decrease more slowly in smaller vials. To test these hypotheses, we measured ethanol solution concentrations in 1,376 vials containing insects collected over an 8-year period and subsequently stored undisturbed for 4–11 yr. The ratio of specimen to ethanol volume was most influential, followed by time in storage, as increasing insect volumes and storage times resulted in lower ethanol concentrations. Cap liner material and vial size did not significantly affect concentration, but human error in ethanol solution mixing and overpacking of specimens in vials created poor preservation conditions. These results can inform collections management methods for ethanol-preserved insects, which are instrumental for keeping specimens scientifically useful in perpetuity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".