Marine Genetic Resources in Areas Beyond National Jurisdiction: Promoting Marine Scientific Research and Enabling Equitable Benefit Sharing
Bibliographic record
Abstract
Growing human activity in areas beyond national jurisdiction (ABNJ) is driving increasing impacts on the biodiversity of this vast area of the ocean. As a result, the United Nations General Assembly committed to convening a series of intergovernmental conferences (IGCs) to develop an international legally-binding instrument (ILBI) for the conservation and sustainable use of marine biological diversity of ABNJ [the biodiversity beyond national jurisdiction (BBNJ) agreement] under the United Nations Convention on the Law of the Sea. The BBNJ agreement includes consideration of marine genetic resources (MGR) in ABNJ, including how to share benefits and promote marine scientific research whilst building capacity of developing states in science and technology. Three IGCs have been completed to date with the fourth delayed by the Covid pandemic. This delay has allowed a series of informal dialogues to take place between state parties, which have highlighted a number of areas related to MGR and benefit sharing that require technical guidance from ocean experts. These include: guiding principles on the access and use of MGR from ABNJ; the sharing of knowledge arising from research on MGR in ABNJ; and capacity building and technology transfer for developing states. In this paper, we explain what MGR are, the methods required to collect, study and archive them, including data arising from scientific investigation. We also explore the practical requirements of access by developing countries to scientific cruises, including the sharing of data, as well as participation in research and development on shore whilst promoting rather than hindering marine scientific research. We outline existing infrastructure and shared resources that facilitate access, research, development, and benefit sharing of MGR from ABNJ; and discuss existing gaps. We examine international capacity development and technology transfer schemes that might facilitate or complement non-monetary benefit sharing activities. We end the paper by highlighting what the ILBI can achieve in terms of access, utilization, and benefit sharing of MGR and how we might future-proof the BBNJ Agreement with respect to developments in science and technology.
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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 | 0.054 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.003 | 0.041 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".