An Evaluation of Prepreparation Storage of Frozen Zoological Specimens
Bibliographic record
Abstract
Abstract Biological specimens for museum collections are acquired because they are important representatives of a species or are critical for ongoing research at an institution. These specimens must be stored in a frozen state until they can be prepared, but the rate of specimen degradation varies under different freezer conditions. If a specimen degrades too much, the types of preparation become limited and the overall quality of the prepared specimen declines. Our objective was to examine the efficacy of various techniques to preserve frozen zoological specimens. To test this, we wrapped chicken wings using various methods and stored them in five different types of freezers. We monitored the mass of the chicken wings over 8 months and documented conditions in each freezer (temperature, relative humidity, door opening frequency and duration). We found desiccation to be the main reason for mass loss from unwrapped specimens, those stored in warmer freezers, and as time in the freezer increased. Therefore, zoological specimens should be prepared soon after being acquired, and stored in plastic bags in the coldest freezer available to minimize desiccation and ensure the ability to prepare them using a variety of preparation methods.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".