Underpinning the <scp>EU</scp> Nature Restoration Regulation: five success factors for effective measures in the Member States
Bibliographic record
Abstract
In August 2024, the Nature Restoration Regulation came into force in the European Union (EU). This landmark legislation on ecosystem restoration may significantly improve the state of European's biodiversity and could profoundly contribute to implementing the Kunming‐Montreal Global Biodiversity Framework. To realize this potential, the EU Member States need to underpin the objectives of the Nature Restoration Regulation with effective measures. Here, we highlight five factors for the success of national nature restoration policies: increased acceptance of nature restoration and landscape change; agreed quantitative and qualified national restoration targets; improved coordination of nature restoration with other land uses; supportive organizational and legal framework conditions; and increased attractiveness of nature restoration to land users and land owners. Drawing on recommendations developed for the German context by three national policy advisory bodies, we suggest that these factors also hold relevance for advancing nature restoration policies in other EU Member States.
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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 | 0.055 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".