Identifying priority areas for terrestrial ecosystem restoration in China
Bibliographic record
Abstract
Global biodiversity loss requires restoration strategies that balance ecological integrity with socioeconomic sustainability. To address Target 2 of the Kunming-Montreal Global Biodiversity Framework, we conducted a nationwide spatial assessment to identify priority areas for terrestrial ecosystem restoration in China, integrating data on degradation, ecosystem services, and socioeconomic activities. About 40% of terrestrial ecosystems are degraded, affecting service-rich regions that cover 47% of the land, including national parks. Under five SSP-RCP scenarios, conflict zones are defined as areas where ecosystem degradation overlaps with socioeconomic activities, and they are projected to cover 42% of the land by 2030. Restoration priorities include the top 30% of ecosystems, spanning water, grassland, forest, arid land, cropland, and urban areas. These zones align with ecological strategies and retain spatial adaptability under future conditions. Integrating ecological and socioeconomic dimensions, this approach offers a framework for planning restoration in biodiversity-rich countries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".