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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".