Prediction of the hydraulic conductivity function for unsaturated soils over the entire suction range
Bibliographic record
Abstract
The hydraulic conductivity function (HCF) refers to in this paper is the variation of hydraulic conductivity over the entire suction range from 0 to 106 kPa. Most of the available HCF models in the literature are derived based on capillary theory and have limitations in the explanation of HCF in the high suction range. Recently, several models have been proposed for accounting for the thin film flow dominated by the adsorbed water in the high suction range. However, these models require measured HCF data for describing the thin film flow, which is time-consuming. In this study, a mathematically continuous and simple model is proposed for predicting the HCF. The model has physically meaningful parameters for predicting the HCF. In this model, the residual suction estimated from the soil–water characteristic curve (SWCC) is used to distinguish the capillary and adsorption-dominated regions, and a correction operator that considers the thin film flow is introduced. The performance of the model was validated using the measured and published experimental data for various soils. In addition, the proposed model is successfully applied in a numerical investigation of the hydraulic behavior of a capillary barrier system, highlighting the role of the thin film flow.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".