Permafrost mitigation solutions for underground thermal energy storage in Baker Lake, Nunavut
Bibliographic record
Abstract
Northern communities in Canada depend on energy to survive harsh arctic and sub-arctic climates. Diesel fuel is their primary energy source, putting these communities in an energetically, economically, and environmentally vulnerable position. Switching from diesel to renewable energy options reduces reliance on fossil fuel and the risk of oil spills. One option is using borehole thermal energy storage (BTES). Waste heat from a diesel generator is stored underground in summer to be extracted during winter. This heat can be provided to the community via district heating. An important issue with BTES in northern Canada is permafrost. Thaw of ice rich material can damage infrastructure such as boreholes. Using permafrost thaw mitigation strategies, it may be possible to limit ground temperature changes causes by a BTES. Numerical simulations of BTES with passive (pipe, grout, VIT casing, air insulation around the upper borehole section) and active solutions (thermosyphons) to mitigate permafrost thaw were developed to address this question. The community of Baker Lake (Nunavut, Canada) is used as an example to define how to reduce ground temperature changes and elevate energy stored. The results have showed that air insulation around the upper section of the borehole and thermosyphons can reduce cyclic changes of ground temperature near the surface caused by underground energy storage.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".