Long-term efficiency of horizontal closed-loop geothermal heat exchangers for stabilization of permafrost beneath a Subarctic lagoon
Bibliographic record
Abstract
Wastewater treatment lagoons are practical and cost-effective systems for small municipalities to reduce nutrient and oxygen release into the environment. However, as they disrupt the natural soil temperatures, they initiate permafrost degradation and cause foundation instability and safety concerns in subarctic regions. In this thesis, the long-term effects of closed-loop horizontal geothermal heat exchangers (GHEs) on the stabilization of permafrost below a wastewater lagoon in northern Canada were studied. This research examined three different geometrical and operational parameters including pipe spacing, heat carrier inlet velocity, and temperature which have the potential to impact the GHE performance in preserving ice-rich permafrost. Thaw settlement was addressed in this context. Also, a machine-learning algorithm was employed to predict unavailable future lagoon temperature based on the currently available weather data. The thesis concludes that the GHE with high-density polyethylene pipes can effectively mitigate and postpone the predicted permafrost thawing under a lagoon. However, under the projected climatic scenario, the GHE system even with every different selected operational parameter fails to eliminate thawing over its lifetime of 50 years. The heat exchangers’ operational parameters substantially affect the permafrost thaw depth. Among all three studied parameters, the heat exchanger fluid temperature is the most influential parameter while the fluid inlet velocity only makes small differences in the thaw depth and thaw settlement.
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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".