A Case Study on the Installation of LLDPE Geomembranes in Cold Weather
Bibliographic record
Abstract
For many reasons, such as ice bridges and operation-down periods, construction on mining sites sometimes needs to happen during winter. Therefore, materials like geomembranes sometimes need to be installed during some of the coldest and harshest weather conditions on earth. However, installing materials such as geomembranes in cold weather presents installation challenges that can compromise the integrity of the liner. Advancements in installation and material technologies have benefited this practice tremendously. One of these innovations is the use of linear low-density polyethylene (LLDPE) geomembranes for these applications—one of its well-known benefits is the reduction of stress crack probability. This paper looks at a cold-weather installation of geomembranes for a mining project in Quebec, Canada. The installation of this geomembrane was performed at temperatures as low as −39°C. This paper discusses the challenges the installation crew faced during this period, as well as how they were overcome. It also compares the installation parameters, such as productivity and special measures, from this phase of the project with the installation parameters of other phases that were done with different materials and/or during warmer periods in the year. Finally, the intent of this case study is to identify good cold-weather installation practices, as well as optimal designs that will allow for proper winter installation of geomembranes for mining projects. Based on these findings and conclusions, this paper makes recommendations on good practices for cold-weather installation and design of polyethylene geomembranes.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".