Vegetation restoration in <scp>N</scp>orthern China: A contrasted picture
Bibliographic record
Abstract
Abstract China started a long‐term effort to mitigate desertification and ensure the sustainability of its environment by implementing multiple large‐scale national ecological restoration projects since 1978, but their success has been highly debated for a long time. Here, we estimated the change of vegetation fraction cover (VFC) in the Three‐North Shelterbelt Programme (TNSP) region over the past three decades on the basis of the Normalized Difference Vegetation Index dataset from the Global Inventory Monitoring and Modeling System. We evaluate the national strategy of vegetation restoration in North China by comparing rainfall patterns, vegetation change, and national ecological restoration programs on the basis of the Global Meteorological Forcing Dataset and the China Forestry Statistical Yearbooks. We find that the western, central, and eastern parts of the TNSP region exhibited a distinct increase in vegetation coverage. The western region had the highest increase of annual precipitation, but this did not result in the highest VFC increase. We infer that ecological restoration activities are the factor leading to the observed increase in VFC in the eastern and central region compared with the western region. The low survival rate of planted trees in the forest of the TNSP region indicates that it is necessary to improve the mode of vegetation restoration to obtain optimal returns and avoid excessive investment. The success of new strategies, for example, natural restoration and quasinatural afforestation are promising as an alternative method. China's experiences in reforestation will be very beneficial for other countries to promote land degradation mitigation and vegetation improvement in the arid and semiarid areas.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".