Working landscapes need at least 20% native habitat
Bibliographic record
Abstract
Abstract International agreements aim to conserve 17% of Earth's land area by 2020 but include no area‐based conservation targets within the working landscapes that support human needs through farming, ranching, and forestry. Through a review of country‐level legislation, we found that just 38% of countries have minimum area requirements for conserving native habitats within working landscapes. We argue for increasing native habitats to at least 20% of working landscape area where it is below this minimum. Such target has benefits for food security, nature's contributions to people, and the connectivity and effectiveness of protected area networks in biomes in which protected areas are underrepresented. We also argue for maintaining native habitat at higher levels where it currently exceeds the 20% minimum, and performed a literature review that shows that even more than 50% native habitat restoration is needed in particular landscapes. The post‐2020 Global Biodiversity Framework is an opportune moment to include a minimum habitat restoration target for working landscapes that contributes to, but does not compete with, initiatives for expanding protected areas, the UN Decade on Ecosystem Restoration (2021–2030) and the UN Sustainable Development Goals.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".