Laboratory Simulation of Bentonite Erosion by Downslope Flow on a GCL
Bibliographic record
Abstract
Under some circumstances, leaving a composite geomembrane/geosynthetic clay liner (GCL) exposed to solar radiation in the field has been shown to cause shrinkage of the underlying GCL. Recent field studies have shown that leaving a composite liner exposed can also lead to erosion of bentonite from the GCL due to downslope moisture migration. This paper reports an experimental technique that reproduced similar erosion in the laboratory on a typical landfill side slope of 3H:1V. The test method simulates the features that occur with the erosion of bentonite caused by downslope migration of evaporative water in the field. The laboratory tests demonstrate that erosion features can be present but may not be visible unless the appropriate back lighting is used. Erosion features measuring over 25 mm in width were produced. The test method simulated the features that were observed with bentonite erosion in the field and has the potential for use in examining other factors that may affect this type of erosion. The test method developed can be used for examining the response of GCLs when part of a composite liner that will be left exposed. The findings from this laboratory study provide additional motivation for timely covering of composite liner systems as recommended by GCL manufacturers.
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 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".