Estimating spatial and temporal water distribution in capillary rise zone of coarse-grained soils based on grain size distribution and unsaturated hydraulic conductivity
Bibliographic record
Abstract
The spatial and temporal distribution of soil water content in the capillary rise zone characterizes fundamental physical behavior associated with capillary rise in soils. Accurate determination of water content distribution during capillary rise is highly necessary for soil mechanics and geotechnical applications. This paper proposed a straightforward and effective model to estimate the water content distribution along with capillary height and time. First, the drying soil–water characteristic curve (SWCC) is derived from the grain size distribution using the scaled MV-VG model. Next, the wetting SWCC is estimated by incorporating hysteresis effects, including contact angle hysteresis and the “ink-bottle” effect, into the drying SWCC to predict the water content distribution with respect to the capillary height. A theoretical solution for the maximum height of capillary rise is then proposed based on the calculated water content distribution. Finally, the model estimates the temporal distribution of water content using a modified Terzaghi's theory, incorporating the wetting unsaturated hydraulic conductivity derived from empirical and statistical models. Extensive comparisons with experimental data demonstrate the excellent accuracy and convenience of the model.
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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 | 0.000 | 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".