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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".