Identifying priority areas for terrestrial ecosystem restoration in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 it