Making space: Putting landscape‐level mitigation into practice in Mongolia
Bibliographic record
Abstract
Abstract Growing resource demands are driving rapid development to new frontiers in developing countries with important biological diversity. The mitigation hierarchy is a critical tool to manage the impacts of development projects on biodiversity, embedded into numerous government, lender, and corporate policies. However, implementation faces obstacles, in particular deciding when impacts should be avoided. Offset design, the last step, faces difficult questions about location of offsets relative to impacts and how to address uncertainty and conflicts with future development. Planning for conservation and development are typically separate processes, and environmental impact assessments are typically conducted on a project‐by‐project basis that does not consider the landscape context and cumulative impacts of multiple projects. Here we present a mitigation framework for Mongolia with an example from the Mongolian Gobi Desert, a landscape with globally significant biodiversity facing rapid development. This landscape‐level planning approach has been replicated across Mongolia to produce a national level mitigation framework to guide both the government policy commitment to protect 30% of all natural lands and application of the mitigation hierarchy. This has led to protection of 177,000 km 2 in new national and local protected areas, and development of an offset design mechanism based on the conservation plans.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| 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".