Effects of Vegetation Type on Soil Shear Strength in Fengyang Mountain Nature Reserve, China
Bibliographic record
Abstract
Shear strength is an important mechanical property of soil, as its mechanical function plays critical roles in reducing land degradation and preventing soil erosion. However, shear strength may be affected by vegetation type through changes in the soil and root patterns. To understand the influences of different types of vegetation on shear strength, the soil shear indices of three typical vegetation types (broad-leaved forest, coniferous broad-leaved mixed forest, and grassland) were studied and evaluated at the Fengyang Mountain Nature Reserve, China. We employed a direct shear apparatus to measure the soil shear resistance index. We quantified the soil porosity, moisture content, and composition of particle size to determine the properties of the soil, and a root scanner was used to quantify the root index. The results revealed that there were significant differences in shear resistance indices at the stand level. Between the three vegetation types, the internal friction angle of the broad-leaved forest was the largest and the cohesion was the smallest. The soil moisture content and porosity of the coniferous broad-leaved mixed forest were higher than those of the broad-leaved forest, and the root volume density (RVD/cm3) of the broad-leaved forest was higher than that of the coniferous and broad-leaved mixed forest and grassland. Structural equation modeling results show that the soil particle size and root characteristics indirectly impacted the soil water content by affecting porosity, which finally affected shear strength. In general, there were significant differences in soil properties and plant root indices between the different stands, which had an impact on soil shear strength.
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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 | 0.001 | 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) | 0.001 | 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".