Soil Water Characteristic Curves Accounting for Soil Swelling and Shrinking
Bibliographic record
Abstract
【Objective】 Heavy-textured soils tend to swell or shrink following rewetting or drying, while most soil water characteristic curves measured from such soils do not consider such effects. The purpose of this paper is to investigate these and proposes an improved model to account for soil swelling and shrink. 【Method】 Changes in soil moisture and matric potential during three wetting-drying cycles in three soils were measured using thermo-TDR probe and soil water potential probe, respectively, and the results obtained from each cycle for each soil were fitted to the van Genuchten formula. We introduced a shrinking percentage parameter to improve the fitting of the van Genuchten formula. 【Result】 The heat pulse probe improved soil volume measurement, with the relative error reduced from 10%~40% to less than 10% and the associated root mean square errors reduced from 0.3~0.4 g/cm3 to less than 0.1 g/cm3. Increasing dry-wet cycles reduced the values of the parameters α and n in the van Genuchten formula. The influence of soil water on soil volume waned as soil water content decreased. The values of the parameters α and n in the van Genuchten formula both decreased as clay content increased. Accounting for the shrinkage in the modified model improved the fitting compared to the original van Genuchten formula that does not consider soil deformation. 【Conclusion】 The heat pulse probe can accurately measure change in soil volume induced by wetting and drying, and the proposed model considering volumetric change of soils following wetting or drying improves the fitting of the van Genuchten formula to the measured data.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".