Developing fencing policies for dryland ecosystems
Bibliographic record
Abstract
Summary In dryland ecosystems, mobility is essential for both wildlife and people to access unpredictable and spatially heterogeneous resources, particularly in the face of climate change. Fences can prevent connectivity vital for this mobility. There are recent calls for large‐scale barrier fencing interventions to address human–wildlife conflict and illegal resource extraction. Fencing has costs and benefits to people and wildlife. However, the evidence available for facilitating sound decision‐making for fencing initiatives is limited, particularly for drylands. We identify six research areas that are key to informing evaluations of fencing initiatives: economics, edge permeability, reserve design, connectivity, ecosystem services and communities. Policy implications . Implementing this research agenda to evaluate fencing interventions in dryland ecosystems will enable better management and policy decisions. The United Nations Conventions on Migratory Species ( CMS ) and to Combat Desertification ( UNCCD ) are appropriate international agreements for moving this agenda forward and leading the development of policies and guidelines on fencing in drylands.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".