Towards a common approach to the assessment of the environmental status of deep-sea ecosystems in areas beyond national jurisdiction
Bibliographic record
Abstract
Many of the marine policy frameworks developed to protect biodiversity in deep-sea areas, including areas beyond national jurisdiction (ABNJ), include indicators to assess policy objectives. These frameworks often have specific guidance on how the indicators should be applied and interpreted. Selection of indicators is an important process and those with strong scientific underpinnings are more likely to produce the expected outcomes. We reviewed three policy and assessment frameworks which include ABNJ regions or were developed specifically for ABNJ: (1) Oslo and Paris Convention for the Protection of the Marine Environment of the North-East Atlantic (OSPAR) ecosystem assessments, (2) the frameworks adopted to implement the UN General Assembly (UNGA) sustainable fisheries resolutions for the management of bottom fisheries to prevent Significant Adverse Impacts on vulnerable marine ecosystems, and (3) the Aichi Biodiversity Targets adopted by Parties to the Convention on Biological Diversity (CBD). We examined whether an assessment approach based on evaluation of Good Environmental Status (GES) under the European Union's Marine Strategy Framework Directive (MSFD), could be applied to ABNJ. We examined each MSFD descriptor for its applicability to deep-sea habitats considering the work of two European projects concluding that the MSFD could be applied to ABNJ to support OSPAR, UNGA and CBD policy objectives towards a common approach to the assessment of the status of deep-sea ecosystems in ABNJ. In achieving this we also introduce readers outside of Europe to the work conducted within the MSFD.
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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 | 0.205 | 0.118 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.035 | 0.019 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.033 | 0.025 |
| Open science | 0.010 | 0.029 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.001 | 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".