Evaluation of multiple time scale rainfall erosivity models: A case study of subtropical regions in Central China
Bibliographic record
Abstract
Abstract Rainfall erosivity is an essential factor affecting soil erosion, which is expected to change under global climate change. Despite the existence of numerous rainfall erosivity models, there remains a scarcity of research focusing on the accuracy of multi‐time scale models. In this study, the subtropical regions of central China (Hubei Province) were selected, where the simulation performance of six widely employed rainfall erosivity models was investigated using daily precipitation data from 70 meteorological stations spanning from 2000 to 2020. Using the optimal model, Kriging interpolation and the Mann–Kendall test revealed significant temporal and spatial variations in rainfall erosivity and density. The results show that: (1) the daily rainfall erosivity model was more suitable for simulating rainfall erosivity in Hubei Province. (2) The mean annual rainfall erosivity in Hubei Province was 5894.25 MJ·mm·ha−1·h−1·a−1, with large variations across regions. (3) Rainfall erosivity and erosivity density showed significant differences between different seasons, and soil erosion was most likely to occur in summer (June, July and August). (4) The spatial distribution pattern of rainfall erosivity and erosivity density was highly consistent: the long‐term high levels of rainfall erosivity and erosivity density were in Xianning City, southeastern Hubei Province, and the soil erosion risk was high. The findings of this study offer valuable insights into the selection of rainfall erosivity models in subtropical mountainous and hilly areas and provide a reference for assessing soil erosion risk and formulating control measures.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".