Analytical solutions for calculating pore-water pressure in an infinite unsaturated slope with different root architectures
Bibliographic record
Abstract
Vegetation can reduce pore-water pressure in soil by root water uptake. The reduction of pore-water pressure results in higher shear strength, but lower soil water permeability, affecting slope stability and rainfall infiltration, respectively. Effects of different root architectures on root water uptake and hence pore-water pressure distributions are not well understood. In this study, new analytical solutions for calculating pore-water pressure in an infinite unsaturated vegetated slope are derived for different root architectures, namely, uniform, triangular, exponential, and parabolic root architectures. Using the newly developed solutions, four series of analytical parametric analyses are carried out to improve understanding of the factors affecting root water uptake and hence influencing pore-water pressure distributions. In the dry season, different root architectures can lead to large variations in pore-water pressure distributions. It is found that the exponential root architecture induces the highest negative pore-water pressure in the soil, followed by the triangular, uniform, and parabolic root architectures. The maximum negative pore-water pressure induced by the parabolic root architecture is about 77% of that induced by the exponential root architecture in the steady state. For a given root architecture, vegetation in completely decomposed granite (CDG, classified as silty sand) induces higher negative pore-water pressure than in either fine sand or silt. The zone influenced by vegetation can be about three to six times the root depth. In the wet season, after a 10 year return period rainfall with a duration of 24 h, different root architectures show similar pore-water pressure distributions.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| 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 teacher head, 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".