Future soil organic carbon stocks in China under climate change
Bibliographic record
Abstract
Quantifying soil organic carbon (SOC) is crucial for China's carbon neutrality goals, yet uncertainties exist due to future climate change. We compiled a comprehensive SOC database for China circa 2010 and utilized digital soil mapping methods to estimate SOC. Using a climate data-driven model, we projected SOC changes from 2021 to 2100 under different shared socioeconomic pathways (SSPs). The top 100 cm SOC is predicted to store 81.99 ± 1.90 to 88.92 ± 1.24 Pg C, with 37.8% to 41.7% in the top 20 cm. Under the SSP119 scenario, the top 100 cm SOC would increase by 11.5 ± 5.3 Tg C year − 1 , contributing to 2.7% ± 1.6% of the carbon sink in China's terrestrial ecosystems over the same period. However, the top 100 cm SOC would transition into a carbon source under SSP245 and SSP585, despite geographical and provincial differences. Maps reveal SOC loss hotspots under SSP245 and SSP585, indicating priority regions for soil carbon conservation efforts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".