Performance of GCLs in high salinity impoundment applications
Bibliographic record
Abstract
The interface transmissivity (θ) and hydraulic conductivity (k) are measured for two geosynthetic clay liners (GCLs), one with polymer-enhanced bentonite, when hydrated and permeated with saline (brine) solutions at three different concentrations and Reverse Osmosis (RO) water. Two interface transmissivity values are reported, the 2-week (θ2-week) and the steady-state (θsteady-state) interface transmissivity. For saline solution (brine) permeation, the 2-week interface transmissivity (θ2-week) is one to two orders of magnitude higher than the steady-state values. In addition, the steady-state interface transmissivity (θsteady-state) with respect to brine is almost an order of magnitude higher than that for RO water permeation. Geomembrane (GMB) stiffness has a limited effect on interface transmissivity at 150 kPa, whereas at 10 kPa the interface transmissivity decreases as the GMB stiffness decreases. GMB texture has only a small effect on interface transmissivity at different stress levels. Water prehydration reduces the effect of brine permeation on interface transmissivity and hydraulic conductivity, especially at 150 kPa. Transmissivity tends to increase as the salt concentration increases but the effect was significant at all concentrations considered (440 to 4400 mmol/l). While the effect of bentonite enhancement on interface transmissivity is unclear, the hydraulic conductivity (k) is generally lower for enhanced bentonite.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".