Effects of soil density on grass-induced suction distributions in compacted soil subjected to rainfall
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Bibliographic record
Abstract
Grass is recognized as being beneficial in reducing rainfall infiltration in some kinds of surface cover systems such as landfill cover, because rainwater discharges as surface runoff due to reduced water permeability caused by evapotranspiration-induced soil suction as well as foliage interception. However, the distributions of grass-induced suction in various compacted soils during rainfall are rarely reported. Moreover, it is not straightforward to determine an optimum soil dry density for minimizing rainfall infiltration and at the same time encouraging plant growth. This is because there are conflicting requirements for vegetated cover systems, i.e., compacted soil should not be too dense as to impede root growth, while on the other hand to minimize infiltration. This study thus aims to investigate, quantify, and compare grass-induced suction distributions in silty sand compacted at different densities when subjected to artificial rainfall in the laboratory. A grass species, Cynodon dactylon, which is common in many parts of Asia, was selected for testing. Compacted soil with and without a growing grass patch was tested at three relative compactions (RCs) of 70%, 80%, and 95%, in six test boxes. Test results reveal that at an RC of 95%, suction (40 kPa) retained in vegetated soil after rainfall is 100% higher than that (20 kPa) in bare soil. Among the vegetated soil compacted at the three RCs, suction retained was the highest at an RC of 95% (40 kPa), whereas suction decreased to 0 kPa at an RC of 70% after rainfall. As the average depth of grass roots decreased by 36% due to an increase in RC from 70% to 95%, the depth of influence of suction for vegetated soil at an RC of 95% reduced to less than half of root depth, which was the shallowest among the three compacted soil specimens.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it