The critical importance of vouchers in genomics
Bibliographic record
Abstract
A voucher is a permanently preserved specimen that is maintained in an accessible collection. In genomics, vouchers serve as the physical evidence for the taxonomic identification of genome assemblies. Unfortunately, the vast majority of vertebrate genomes stored in the GenBank database do not refer to voucher specimens. Here, we urge researchers generating new genome assemblies to deposit voucher specimens in accessible, permanent research collections, and to link these vouchers to publications, public databases, and repositories. We also encourage scientists to deposit voucher specimens in order to recognize the work of local field biologists and promote a diverse and inclusive knowledge base, and we recommend best practices for voucher deposition to prevent taxonomic errors and ensure reproducibility and legality in genetic studies.
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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 | 0.132 | 0.282 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.011 | 0.029 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".