Field measurements of water storage capacity in a loess–gravel capillary barrier cover using rainfall simulation tests
Bibliographic record
Abstract
A 30 m long × 20 m wide capillary barrier cover (CBC) test site was constructed at the Jiangcungou landfill in Xi’an, China. The cover consisted of a compacted loess layer with a thickness of 0.9 m underlain by a gravel layer. After the cover surface was kept bare and exposed to natural climate conditions for nearly 5 months, one artificial rainfall event was implemented at the site. Vegetation was established at the test site after the first rainfall event. Four months later, a second artificial rainfall event was applied to the surface of the vegetated site. The pore-water pressures (PWPs) and volumetric water contents (VWCs) of the cover were monitored using jet-filled tensiometers and time-domain reflectometry moisture probes, respectively. Surface runoff and percolation were measured using field collection devices. The field measurements demonstrated a more rapid response of PWPs to the rainfall compared to the response of the VWCs. Percolation was observed when the PWPs near the interface reached the water-entry value of the gravel at local points. At that moment, the measured VWC near the interface was less than the VWC according to the water-entry value. The observation indicated that preferential flows took place in the compacted loess during the rainfall. As a result, the maximum water storage capacity was not reached at the onset of percolation. When percolation ceased, the average PWP near the interface decreased below the water-entry value, while the VWC near the interface was higher than that at the onset of percolation. Water storage at the completion of percolation was approximately 5% greater than that at the onset of percolation. Compared with the monolithic loess cover, the loess–gravel CBC increased the available water storage capacity by 41% at the completion of percolation. Vegetation had an insignificant influence on water storage capacity.
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How this classification was reachedexpand
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 | 0.000 |
| 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 teacher head, 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".