Rural–Urban Migration and Conservation Drive the Ecosystem Services Improvement in China Karst: A Case Study of HuanJiang County, Guangxi
Bibliographic record
Abstract
Under the transformation from over-cultivation to ecological protection in China’s karst, how human activities affect ecosystem services should be studied. This study combined satellite imagery and ecosystem models (Carnegie-Ames-Stanford Approach (CASA), Revised Universal Soil Loss Equation (RUSLE) and Integrated Valuation of Ecosystem Services and Trade-offs (InVEST)) to evaluate primary ecosystem services (net ecosystem productivity (NEP), soil conservation and water yield) in a typical karst region (Huanjiang County). The relationships between human activities and ecosystem services were also examined. NEP increased from 441.7 g C/m2/yr in 2005 to 582.19 g C/m2/yr in 2015. Soil conservation also increased from 4.7 ton/ha to 5.5 ton/ha. Vegetation recovery and the conversion of farmland to forest, driven largely by restoration programs, contributed to this change. A positive relationship between increases in NEP, soil conservation and rural-urban migration (r = 0.62 and 0.53, P < 0.01, respectively) indicated decreasing human dependence on land reclamation and naturally regenerated vegetation. However, declining water yield from 784.3 to 724.5 mm highlights the trade-off between carbon sequestration and water yield should be considered. Our study suggests that conservation is critical to vegetation recovery in this region and that easing human pressure on land will play an important role.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".