Renewable energy-based artificial ground freezing as an adaptation solution for sustainability of permafrost in post-climate change conditions
Bibliographic record
Abstract
Abstract Climate change is expected to impose higher ground temperatures, seriously challenging the sustainability of permafrost regions by thawing irreversibly, compromising ground stability and causing high seepage flows. Mining operations are particularly vulnerable to permafrost removal, and in extreme cases may face catastrophic consequences in their waste management systems, such as tailings dams. So far, artificial ground freezing has been promoted as a reliable and technologically possible solution to maintain permafrost against raises in ground temperature. However, considerable amounts of electric power are required which can be challenging especially in remote areas. A solution can be sought by taking advantage of cold winter temperatures to provide artificial ground freezing. In this renewable energy-based technique, thermosyphons use subfreezing winter temperatures to create enough freezing in the permafrost layer which can last during the summer as well. The present paper underlines the importance of developing the proposed technology and evaluates its techno-economic feasibility through numerical and experimental studies. It offers a numerical model for a renewable energy-based artificial ground freezing system and validates its results against laboratory experiments. The results suggest that the utilization of thermosyphon along with cold-energy storage increases and maintains the thickness of the permafrost, especially during the summer season.
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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".