Evaluating the influences hedgerow on soil erosion and nitrogen loss of purple soil sloping farmland under simulated rainfall
Why this work is in the frame
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Bibliographic record
Abstract
Hedgerow is a vital soil and water conservation technology for sloping farmland that can significantly reduce erosion and nutrient loss via their above- and below-ground parts. The effect of hedgerow on erosion and nutrient loss has been widely investigated, while the respective effects of their above- and below-ground parts are still unclear. Therefore, the purple soil from the Three Gorges Reservoir Area was used, and 3 slope conditions of control check, whole hedgerow and hedgerow roots only combined with 2 slope gradients (15° and 25°) were constructed and the simulated rainfall tests were researched at 3 rainfall intensities (60, 90 and 120 mm h−1). The runoff initiation time, runoff and erosion rates, and loss of nitrogen via runoff and sediment were analysed. In comparison to the control check slope condition, it was indicated that hedgerow increased the runoff initiation time by 43.38 %, decreased runoff and erosion by 15.59 % and 78.37 %, and decreased nitrogen loss via runoff by approximately 40 % and via sediment by approximately 70 % on average, respectively. The average contribution rates of the below-ground part of the hedgerow was 49.89 % for the increase in runoff initiation time, 33.99 % for runoff reduction, and 39.91 % for erosion reduction. In addition, more than 2/3 and 58.49 % of nitrogen loss reduction via runoff and sediment, respectively, was contributed by the above-ground part of the hedgerow. These results are beneficial for comprehending the control mechanism of hedgerow on soil erosion and nitrogen loss, thereby provide a scientific foundation for the sustainable and efficient utilization of soil and water resources on sloping farmland.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it