Factors affecting drying and wetting soil-water characteristic curves of sandy soils
Bibliographic record
Abstract
Drying and wetting soil-water characteristic curves (SWCCs) for five sandy soils are investigated using a Tempe pressure cell and capillary rise open tube. The test data are fitted to two SWCC equations using a least-squares algorithm. The obtained fitting parameters and some hysteretic behaviour are discussed and correlated with grain-size distribution parameters. A concept of total hysteresis is proposed to quantify the hysteresis of SWCC. The measured SWCC for one soil is also compared with the SWCC estimated from its grain-size distribution. The SWCC was also obtained at a high dry density for one of the soils. The results show that the shapes of the SWCCs are similar to the grain-size distributions of the soils and are affected by the dry density of the soil. A coarse-grained soil has a lower air-entry value, residual matric suction, and water-entry value and less total hysteresis than a fine-grained soil. The residual matric suction and water-entry value tend to approach the same value when the effective grain size D10of the soil is small, in the range of 3-6 mm. SWCCs of uniform soils have steeper slopes and less total hysteresis than those of less uniform soils. Soils with a low dry density have a lower air-entry value and residual matric suction than soils with a high dry density. The SWCC predicted from grain-size distribution is found to be sufficiently accurate.Key words: soil-water characteristic curve, water content, suction, hysteresis, grain size.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".