Dimensional analysis of pore-water pressure response in a vegetated infinite slope
Bibliographic record
Abstract
Pore-water pressure (PWP) induced by root water uptake has usually been investigated by individual physical quantities. Limited dimensional analysis has been available for investigating PWP response in a vegetated slope. In this study, dimensional analysis was conducted to explore dimensionless numbers controlling PWP distributions in a vegetated slope. Three dimensionless numbers governing unsaturated seepage were proposed, including capillary effect number (CN, describing the relative importance of water flow driven by PWP gradient over that driven by gravity), root water uptake number (RN, representing the effects of root water uptake), and water transfer–storage ratio (WR, ratio of water transfer to water storage rate). Dimensionless relationships were further proposed to estimate PWP and root influence zone in a vegetated slope. Then analytical parametric studies were conducted to study effects of RN, CN, and WR on PWP distributions. Thereafter, the proposed relationships were validated by published field and centrifuge tests. During the drying period, the effects of root water uptake on PWP and the root influence zone become more significant as CN decreases or RN increases. During the wetting period, the larger the WR, the deeper the wetting front moves and more reduction of negative PWP occurs. The proposed dimensionless relationships can determine PWP and the root influence zone in a vegetated soil reasonably well.
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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".