Best practices for genetic resources associated with natural history collections: Recommendations for practical implementation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it