Rural outmigration generates a carbon sink in South China karst
Why this work is in the frame
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Bibliographic record
Abstract
China karst is a global hotspot of increasing vegetation cover, with ecological conservation projects being considered as the main driver. New research using global datasets also indicates that rural outmigration has contributed to increasing biomass at national scale. However, the link between rural outmigration and vegetation cover increase has not been established at regional scale, and it remains unclear as to whether increases in biomass do, in fact, improve the environmental conditions. In this study, we use local field and statistical data on population density and rocky desertification areas to study population movements and changes in aboveground biomass in relation to rocky desertification in South China karst during 2000–2017. Our results show that the urban population in this region increased by 8.3 million people between 2005 and 2015, and the rural population decreased by 4.8 million people. We find that aboveground biomass increased most in rural areas with low human pressure, and that there was an almost linear relationship between increase in biomass and rural outmigration, with the highest increase in aboveground biomass density (1.5 MgC ha −1 yr −1 ) observed in areas where rural outmigration was highest, and the lowest increase in aboveground biomass density (1.1 MgC ha −1 yr −1 ) where rural outmigration was lowest. Rocky desertification areas decreased with a higher level of rural outmigration. Using local field data, our study confirmed that rural outmigration can generate a carbon sink at regional scale by reducing rocky desertification.
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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 | 0.000 | 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