A policy-based framework for the determination of management options to protect vulnerable marine ecosystems under the EU deep-sea access regulations
Bibliographic record
Abstract
Abstract Vulnerable marine ecosystems (VMEs) are particularly susceptible to bottom-fishing activity as they are easily disturbed and slow to recover. A data-driven approach was developed to provide management options for the protection of VMEs under the European Union “deep-sea access regulations.” A total of two options within two scenarios were developed. The first scenario defined VME closure areas without consideration of fishing activity. Option 1 proposed closures for the protection of VME habitats and likely habitat, while Option 2 also included areas where four types of VME geophysical elements were present. The second scenario additionally considered fishing. This scenario used VME biomass—fishing intensity relationships to identify a threshold where effort of mobile bottom-contact gears was low and unlikely to have caused significant adverse impacts. Achieving a high level of VME protection requires the creation of many closures (> 100), made up of many small (∼50 km2) and fewer larger closures (> 1000 km2). The greatest protection of VMEs will affect approximately 9% of the mobile fleet fishing effort, while closure scenarios that avoid highly fished areas reduce this to around 4–6%. The framework allows managers to choose the level of risk-aversion they wish to apply in protecting VMEs by comparing alternative strategies.
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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 | 0.037 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.003 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".