Efficacy of Pond Ash as a Cover Material in Single and Dual Capillary Barriers
Bibliographic record
Abstract
In this paper, numerical study results that highlight the capability of using pond ash as a cover material in covers with capillary barrier effects are succinctly summarized. A model was exclusively developed and calibrated using commercial software SEEP/W for this study. After calibration, the efficiency of pond ash as a coarse-grained layer (CGL), fine-grained layer (FGL), unsaturated drainage layer (UDL), and seepage control layer (SCL) was investigated using various combinations. The CCBE made of pond ash obtained from the output and input points of an ash pond as alternate fine- and coarse-grained layers were observed to perform well. The performance of pond ash as UDL and SCL was also observed to be good. The approach presented in this study is valuable in assessing likely slope failures of coal ash storage facilities that may be triggered by rainfall events. The study aids in significantly reducing rainfall infiltration, improving the overall stability of the slopes, and promoting sustainability by utilising the concept of “waste covering waste”. The approaches used in this study can be extended in the rational design of slopes to address the future challenges anticipated with unprecedent rainfall events and its negative impacts associated with climate change effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".