A study of hysteresis models for soil-water characteristic curves
Bibliographic record
Abstract
A review of hysteresis models for soil-water characteristic curves is presented. The models can be categorized into two groups: (i) domain models (or physically based models) and (ii) empirical models. Some models are capable of predicting scanning curves, while other models are capable of predicting the boundary wetting curve and the boundary drying curve. A comparison of the ability of five selected models to predict the boundary wetting curve showed that the Feng and Fredlund model with enhancements by Pham, Fredlund, and Barbour appears to be the most appropriate model for engineering practice. Another comparison among five physically based models for predicting scanning curves showed that the Mualem model-II gives the best overall prediction of scanning curves. The study showed that taking the effect of pore blockage into account does not always give a better prediction of hysteretic soil-water characteristic curves. A scaling method for estimating the initial drying curve, the boundary wetting curve, and the boundary drying curve is also presented in the paper.Key words: soil-water characteristic curve, hysteresis model, comparison, boundary curve, scanning curve, unsaturated soils.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".