Impacts of Corn Straw Coverage and Slope Gradient on Soil Erosion and Sediment Size Distributions in the Mollisol Region, NE China
Bibliographic record
Abstract
Abstract The Chinese Mollisol region has the highest fertility and productivity in the country but also suffers from soil erosion. However, the characteristics of soil erosion and sediment size distributions in different seasons based on natural croplands with or without corn straw coverage at different slope gradients remain unclear. This study investigated the effects of corn straw coverage (0, 50, and 100%) and slope gradients (5° and 10°) on the soil water content, bulk density, runoff, soil loss, and eroded sediment size in autumn and the following spring in the Mollisol region of NE China. Natural runoff plots, which were 20 m long and 5 m wide, were used to conduct inflow scour (l L min–1 m–2) experiments. The results showed that the soil water contents generally increased and that the bulk densities decreased in spring compared with those in autumn. For treatments without corn straw coverage, the runoff values and soil losses in spring were 1.2–6.7 and 3.4–25.0 times greater than those in autumn, respectively. For treatments with corn straw coverage, the runoff was effectively decreased under 50% corn straw coverage for the 5° croplands but under 100% corn straw coverage for the 10° croplands; the soil losses were 0 for the 5° croplands and increased 2.3–2.6 times for the 10° croplands in spring compared with those in autumn. As the slope gradient varied from 5° to 10°, the runoff and soil losses significantly increased, but the loss of <0.25 mm soil materials in proportion to the soil erosion generally decreased. The seasonal changes affected the soil material size distributions of the eroded sediment, especially for finer sediments. In conclusion, soil conservation measures used to decrease the slope gradient and seasonality impacts on soil erosion and sediment size distributions in the Mollisol croplands are noteworthy.
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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 | 0.000 | 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 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".