Restoration priorities to achieve the global protected area target
Bibliographic record
Abstract
Abstract With much of Earth's surface already heavily impacted by humans, there is a need to understand where restoration is required to achieve global conservation goals. Here, we show that at least 1.9 million km 2 of land, spanning 190 (27%) terrestrial ecoregions and 114 countries, needs restoration to achieve the current 17% global protected area target (Aichi Target 11). Restoration targeted on lightly modified land could recover up to two‐thirds of the shortfall, which would have an opportunity cost impact on agriculture of at least $205 million per annum (average of $159/km 2 ). However, 64 (9%) ecoregions, located predominately in Southeast Asia, will require the challenging task of restoring areas that are already heavily modified. These results highlight the need for global conservation strategies to recognize the current level of anthropogenic degradation across many ecoregions and balance bigger protected area targets with more specific restoration 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".