Implications of the Nagoya Protocol for genome resource banks composed of biomaterials from rare and endangered species
Bibliographic record
Abstract
The Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits Arising from their Utilisation is a multilateral legal instrument within the Convention on Biodiversity. It has now come into force, having been signed by 92 countries, 68 of which have ratified it, but notably these do not yet include the US, China, Canada and Russia. The overarching objective of the Nagoya Protocol is to prevent the unfair commercial exploitation of a country's biodiversity and it also protects traditional knowledge. Although the intentions seem reasonable and equitable, the provisions of the Nagoya Protocol will have major effects on the ability of researchers in both the commercial and non-commercial sectors to access genetic materials (which are widely defined and include almost every conceivable animal product, as well as whole animals) from around the world. It also places a heavy bureaucratic burden on researchers and their institutions, which must comply with an international standard and obtain an International Certificate of Compliance proving that all samples will be collected according to the terms of the Protocol. Herein we review of the unforeseen implications of the Nagoya Protocol in relation to biobanking and animal conservation.
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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 | 0.032 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".