Proposing a socialecological framework for successful grassland restoration in Germany – an overview and insights from the Grassworks project
Bibliographic record
Abstract
Bending the biodiversity curve and delivering on biodiversity promises from international agreements and laws, including Kunming-Montreal and the EU Restoration Law, requires upscaling ecological restoration from smaller to larger spatial and temporal dimensions and across different spheres of society. Achieving this depends on a strong scientific evidence base and synthesis of effective practices from both ecological and social perspectives. The Grassworks project investigates the factors driving success in grassland restoration in Germany, addressing ecological, socio-economic, and socialecological dimensions. We address this by conducting a post-hoc assessment of previously restored sites, comparing them to both positive and negative reference sites across three regions along a north-south gradient in Germany. In the post-hoc assessment, we employed a stratified design to evaluate the effects of restoration methods, previous land use, current management, governance, finance, and time since restoration intervention. We assessed vegetation, butterflies, wild bees, soil characteristics, and economic performance, while controlling for surrounding landscape configuration. Additionally, we examined key socialecological dimensions, including stakeholder values, knowledge exchange, and decision-making processes within established networks. This was complemented by a Real-World Laboratory approach, integrating ex-ante and ex-post assessments, demonstration sites, and live restoration activities co-created with local stakeholders. This publication provides an overview and reflection, drawing on insights from the Grassworks project in Germany, to inform, guide and support the development of future large-scale socialecological restoration efforts worldwide.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".