Best practices for genetic resources associated with natural history collections: Recommendations for practical implementation
Bibliographic record
Abstract
Abstract Researchers associated with natural history museums have made the collection of genetic resources a priority due to their importance in molecular studies, but often the long-term curation of these collections is difficult due to decentralized curation over multiple storage locations and lack of community best practice guidelines for their stewardship. Unlike traditional natural history specimens, the research utility of genetic samples increases with lower storage temperatures and fewer freeze–thaw events and, in addition, their use is consumptive. Collection managers must, therefore, maximize the research potential of each sample by carefully considering use on a case-by-case basis. This paper presents standardized guidelines accumulated for the management of genetic collections associated with natural history collections. These recommended practices are informed by general standards for biorepositories and augmented by information unique to natural history collections with the goal of providing a foundation for those curating genetic samples. Information pertains to all aspects of genetic sample curation and will assist those in making decisions regarding how to collect, store, track, process, and distribute genetic specimen samples. These guidelines also will allow users to make informed decisions regarding how to apply and improve the curation of their collection given their institution's goals and available resources.
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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 | 0.253 | 0.352 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.015 | 0.011 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".