Using Scenarios for Reducing Uncertainties in Biodiversity Conservation: From Global Targets to European Steppes
Bibliographic record
Abstract
ABSTRACT Scenario analysis allows assessing how complex socio‐ecological systems might respond to different policy pathways. We used an expert‐based participatory approach to explore how four different European socio‐ecological scenarios could impact (1) the implementation of the Kunming–Montreal Global Biodiversity Framework (KM‐GBF) and (2) the achievement of priority conservation actions for safeguarding European steppe ecosystems. KM‐GBF targets were expected to be met only under the scenario with increased commitment for sustainable development goals and global cooperation, but hardly achievable under the most environment‐adverse and resource‐demanding scenarios. Integrating different views from these scenarios, we identified six overarching recommendations for the conservation of European steppe ecosystems, including improving public awareness, empowering local communities, and promoting the engagement of private companies into conservation planning. Our approach identifies how socioeconomic drivers influence the success of the KM‐GBF and the conservation of European steppes, providing a range of general conservation actions structured and prioritized to be effective under a wide range of likely future developments.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".