Factors Affecting the Spatial and Temporal Variations in Soil Erodibility of China
Bibliographic record
Abstract
Abstract Soil erosion occurs extensively across China, leading to severe degradation of the land and ecosystem services. However, the spatial and temporal variations in soil erodibility (k) and the distribution of soil erosion across land use types and slopes remain unclear. We synthesized the results from 325 sites published in 152 literatures to analyze the factors affecting the k, such as land use type, climate, topography, soil, and vegetation restoration age. The results showed that areas with slopes >25° had a larger k factor (k = 0.1047) than did those with slope <6° (k = 0.0637) or 6–25° (k = 0.0832). The k from 2006 to 2011 (k = 0.0725) was higher than that from 1999 to 2005 (k = 0.058) and that from 2012 to 2016 (k = 0.0631). The k value initially increased with vegetation restoration age and then gradually decreased. Land use also had an impact on the k factor, with the k factor of cropland (k = 0.0697) being higher than that of grassland (k = 0.0663) but lower than that of forest (k = 0.0967). Across China, North Shaanxi, Heilongjiang, and South Guizhou, which are located in the Loess Plateau in Northwest China, the Black Soil region of Northeast China, and the Karst areas in Southwest China, respectively, were the three most severely eroded regions due to hydraulic erosion, frost‐thaw erosion, and high‐intensity erosion, respectively. Overall, the most important factors affecting the k were soil characteristics, followed by topography and climate. Among them, soil nitrogen and precipitation were the two most critical factors influencing the k.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".