A comparison of geomembrane wrinkles for nine field cases
Bibliographic record
Abstract
ABSTRACT: The length of the longest wrinkles formed in black, exposed high-density polyethylene (HDPE) geomembranes are compared for nine different field cases for a range of different conditions (base or slope; GCL, CCL (geosynthetic clay liner) or sand subgrade below geomembrane; geomembrane thicknesses of 1.5 mm and 2 mm, and textured or smooth geomembrane). The size of the geomembrane area that was restrained was shown to limit the longest connected wrinkle that developed. Of the cases examined, the longest wrinkle was 5330 m for a 1.5 mm-thick geomembrane on sand on a 0.61 ha west-facing 3H:1V side slope. On the smallest geomembrane areas (less than 0.09 ha in this study), the longest observed connected wrinkles throughout the day were less than 550 m. The average wrinkle widths were 0.20–0.23 m and 0.24–0.32 m resting on a GCL and CCL, respectively. Wrinkle height was found to average 0.06 m and the maximum height was 0.18 m. When less than 8% of the geomembrane was wrinkled and the sum of the wrinkle lengths was less than 600 m, the longest connected wrinkle was less than 200 m. Data from these nine cases show that, on sunny days, restricting covering of the geomembrane to the early morning or late afternoon and/or reducing the restrained area of geomembrane to 0.05 ha (or less) will minimise the risk of the formation of long connected wrinkles for environments similar to those examined.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".