A pore-water distribution model for the estimation of the hydraulic conductivity of stiff unsaturated soil during the wetting process
Bibliographic record
Abstract
It is noted that not only the soil–water characteristic curve (SWCC) exhibits hysteresis in the drying and wetting processes, but the hydraulic conductivity of unsaturated soil also shows a similar characteristic. The experimental measurements of the hydraulic conductivity of unsaturated soil either in the drying or wetting process are commonly time-consuming and costly. Therefore, the hydraulic conductivity of unsaturated soil is commonly determined by using the indirect method. In the previous studies, the drying hydraulic conductivity function (HCF d ) is commonly estimated from the drying SWCC based on the concept of the pore-size distribution. In this study, a new pore-water distribution-based model was proposed to estimate the wetting hydraulic conductivity function (HCF w ) of unsaturated soil. In the proposed model, several factors such as the “rain-drop” effect, “ink-bottle” effect, and entrapped air that may lead to the hysteresis of SWCC were incorporated in the computation of the pore-water distribution in soil during the wetting process. Subsequently, the statistical method was adopted to compute the effective area that allows water flow in an unsaturated soil based on the information of pore-water distribution in the soil. Consequently, a new pore-water distribution-based model was proposed to estimate the HCF w of unsaturated soil. The proposed model shows good agreement with the experimental data from various published literatures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".