The effects of three micro-catchment practices on erosion and runoff dynamics for a typical soil slope on the Loess Plateau of China
Bibliographic record
Abstract
Three micro-catchment measures that are named fish-scale pits (FSPs), artificial digging (AD), and contour plowing (CP) for soil erosion prevention are widely used in the Loess Plateau. To clarify the effectiveness of these measures in intercepting runoff and reducing erosion and the mechanism of water flow movement, intermittent simulated rainfall events was carried out in the 15° slopes with FSPs, AD, CP, and control slope (CK). The results demonstrated the following. (1) For cumulative rainfall <83 mm, three measures effectively intercepted runoff and reduced sediment compared with the CK. The runoff and sediment reduction effect of three measures gradually disappeared when cumulative rainfall increased to 83, 99, and 108 mm, and the sediment generation of the three measures successively exceeded that of the CK and was more than two times higher. (2) Laminar or transition flow occurred for the CK, and the flow pattern changed from subcritical to supercritical at 101 mm of cumulative rainfall. For three measures, the flow patterns became turbulent within a short time but remained subcritical. (3) A correlation analysis showed that the soil detachment rate, hydraulic shear stress, and stream power in the micro-catchment measures can be described using linear functions, which reduced the rill erodibility and enhanced the soil’s resistance to concentrated flow erosion. This research has important guiding significance on the rational and effective implementation of micro-catchment practices to prevent severe soil erosion and increase water storage for crop production on the Loess Plateau of China.
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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 | 0.000 | 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 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".