D1.2 Report on Atlantic and Arctic Policy and Governance Frameworks
Bibliographic record
Abstract
Abstract Marine ecosystems are essential to global biodiversity and ecological stability, yet face mounting threats from human activity and climate change. In response, international and regional frameworks—including the Convention on Biological Diversity’s Kunming-Montreal Global Biodiversity Framework and the EU Biodiversity Strategy 2030—have set ambitious targets to protect 30% of marine areas by 2030, with one-third under strict protection. The EU Mission “Restore Our Ocean and Waters” reinforces these goals through regional “Lighthouses,” each focused on tailored restoration and protection objectives. This report centers on BlueMissionAA, the coordination and support action for the Atlantic and Arctic Lighthouse, which serves as a strategic hub for implementing the Mission’s objectives in these regions. It presents a comprehensive governance baseline, mapping policy frameworks and area-based management measures (MPAs and OECMs), and identifies 17 key observations across strategic direction, legal frameworks, and implementation mechanisms. A cross-regional comparison reveals significant variation in governance approaches, which may influence the effectiveness of Mission delivery. Through a multi-tiered analysis—regional and national—the report assesses the extent to which existing governance structures support the Mission’s goals. It offers reflection points to improve coherence and impact, including shifting from quantitative targets to ecosystem-based approaches, integrating marine protection into broader strategies, reinforcing legal alignment across sectors, and promoting participatory processes. These insights, supported by case studies on ecological restoration, will inform the Expert Panel’s work in shaping the Mission’s implementation phase. Overall, the report provides a foundational resource for advancing governance and restoration efforts in the Atlantic and Arctic basins.
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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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".