Field and laboratory investigation of factors affecting GCL performance in the Antarctic environment
Bibliographic record
Abstract
Performance indicators of geosynthetic clay liners (GCLs) exposed to the Antarctic environment were assessed using field and laboratory methods. Barrier properties of the GCLs were not impacted by the field conditions and exhumed hydraulic conductivities with respect to tap water ranged from 0.4 × 10−11 to 12 × 10−11 m/s, compared to 5 × 10−11 m/s (virgin) and from 2.1 × 10−11 to 8.8 × 10−11 m/s for a synthetic leachate, compared to 2.5 × 10−11 to 7.6 × 10−11 m/s for virgin specimens. The self-healing ability of bentonite remained intact despite environmental exposure as evidenced by X-ray radiographs. Using a test plot to evaluate GCL hydration with the major determinant of hydration was identified as proximity to meltwater flow. However, a finer subgrade ( D60 = 5–7 mm) increased average and peak GCL moisture contents in areas not inundated with meltwater and resulted in less deformation than a coarse subgrade ( D60 > 16 mm). Additionally, X-ray radiographs showed increased self-healing in monitoring specimens overlying fine subgrades. Environmental exposure (e.g., temperature cycling and dehydration/rehydration) did not affect the hydraulic conductivity of test plot specimens (1.1 × 10−11 to 8.5 × 10−11 m/s) compared to the virgin GCL (6.7 × 10−11 m/s) with respect to tap water, and minimal cation exchange had occurred.
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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 | 0.000 | 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".