TOWARDS CLIMATE-SMART REWILDING: AN INTEGRATED FRAMEWORK FOR BIODIVERSITY, CLIMATE CHANGE, AND SOCIETY
Bibliographic record
Abstract
ABSTRACT The UN Decade on Ecosystem Restoration and the Kunming-Montreal Global Biodiversity Framework aim to restore 30% of degraded ecosystems. Both the IPCC and IPBES highlight the crucial role of ecosystem restoration in addressing the interconnected crises of climate change and biodiversity loss. One key restoration strategy is rewilding, which enhances ecosystem complexity with minimal human intervention. While traditional rewilding strategies often focus on benefits for biodiversity, we propose a climate-smart rewilding framework as a new approach designed to deliver climate benefits alongside biodiversity restoration. This framework seeks to integrate biodiversity, climate change adaptation and mitigation, as well as socio-economic benefits and trade-offs. We illustrate how this framework can be utilized to identify areas across Europe where rewilding could increase carbon sequestration, enhance species’ abilities to adapt to the velocity of climate change, and maximize wildlife-watching benefits while minimizing the costs associated with livestock-wildlife conflict. Finally, we acknowledge some limitations of climate-smart rewilding, but we argue that its adaptability and low cost render it a promising solution to the challenges facing Europe and beyond.
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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 | 0.020 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".