Best practices for collecting and preserving marine mammal biological samples in the ‘omics era
Bibliographic record
Abstract
Abstract The recent rise of ‘omics and other molecular research technologies alongside improved techniques for tissue preservation have broadened the scope of marine mammal research. Collecting biological samples from wild marine mammals is both logistically challenging and expensive. To enhance the power of marine mammal research, great effort has been made in both the field and the laboratory to ensure the scientific integrity of samples from collection through processing, supporting the long‐term use of precious samples across a broad range of studies. However, identifying the best methods of sample preservation can be challenging, especially as this technological toolkit continues to evolve and expand. Standardizing best practices could maximize the scientific value of biological samples, foster multi‐institutional collaborative efforts across fields, and improve the quality of individual studies by removing potential sources of error from the collection, handling, and preservation processes. With these aims in mind, we summarize relevant literature, share current expert knowledge, and suggest best practices for sample collection and preservation. This manuscript is intended as a reference resource for scientists interested in exploring collaborative studies and preserving samples in a suitable manner for a broad spectrum of analyses, emphasizing support for ‘omics technologies.
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 | 0.112 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".