When to cryopreserve and when to let it go? A systematic review of priorities in wild animal cryobanking
Bibliographic record
Abstract
With increasing numbers of species threatened with extinction, collecting and conserving living samples is important for the long-term conservation of animal populations. Globally, many cryobanks have been developed to preserve animal tissues for future use in wildlife conservation. However, to date, there has been no attempt to review the purpose, priorities and direction of these cryobanks. A systematic review was undertaken using Web of Science, Scopus, and Google Scholar to determine the most common priorities identified in the cryobanking literature. The types of species being recommended for cryobanking, cell types, and recommended numbers of samples and number of individuals were also recorded for cryobanking efforts. Overall, 13,287 papers were identified, of which 794 were selected for full-text review. For wildlife, the most frequently cited priority was to select based on threat level, with convenience sampling and genetic diversity featuring as the second and third most common priorities. In terms of cell type, sperm featured most frequently in cryobanking literature, potentially due to its ease of use in animal breeding programmes. Somatic cells and stem cells featured more commonly in more recently published literature. Looking ahead, cryobanks should consider their priorities and records to ensure they are collecting samples with a meaningful use for future conservation efforts. Greater collaboration between cryobanks can aid in important sample acquisition and storage and in sharing cryopreservation priorities. • The most commonly cited priority for selecting wildlife species for cryobanking was to select species based on threat status. • The most common priority when selecting within a species was to focus on genetic diversity. • Sperm cells featured as the most common cell type for cryopreservation, though oocytes, embryos and somatic cells also commonly featured.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".