Field study of rainfall infiltration into a grassed unsaturated expansive soil slope
Bibliographic record
Abstract
A full-scale field study was conducted to investigate the effects of rainfall infiltration on a natural grassed expansive soil slope in China. A 16 m wide × 28 m long area was selected for instrumentation. The instrumentation included jet-filled tensiometers, moisture probes, a tipping bucket rain gauge, and a vee-notch flow meter. One artificial rainfall event amounting to about 370 mm rain depth in total was applied to the slope. The monitored results suggested that there was about a 3 day delay in the response of surface runoff, pore-water pressure, and water content to the commencement of the simulated rainfall. The depth of influence of the rainfall, depending on the elevation along the slope, ranged from 2.8 to 3.5 m. Positive pore-water pressures were measured within the influence depth, and there existed significant subsurface downslope flow at the end of the simulated rainfall, particularly near the lower part of the slope. A comparison of infiltration rates between the grassed area and a bare area nearby indicated that the presence of grass significantly increased the infiltration rate and reduced surface runoff. The cracks and fissures developed in the unsaturated expansive soil played an important role in the hydrological process.Key words: expansive soil, slope instability, infiltration, vegetation cover, grass, soil suction, water content, unsaturated soil.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".