A holistic approach towards the integration of geothermal energy in remote northern communities
Bibliographic record
Abstract
Geothermal energy is increasingly considered as an energy alternative across off-grid indigenous communities in northern Canada. These communities primarily rely on diesel for electricity and a combination of oil, propane, wood, and diesel for heating. Burwash Landing, the seat of the Kluane First Nation government in Yukon Territory, Canada, is located on the shore of Łù'àn Män (Kluane Lake) at the base of the St. Elias Mountains and near a step-over in the Denali fault. A Play fairway analysis of southwestern Yukon highlights the geothermal favourability around Burwash Landing.Over the past 15 years, Kluane First Nation has taken significant steps to reduce greenhouse gas emissions and drilled a community-led geothermal exploration borehole in 2012 (KFN‑L; 387 m). KFN‑L was drilled northeast of the Denali fault in Quaternary sediments. In 2021, the Yukon Geological Survey drilled a second exploration borehole (DRGW; 220 m) in bedrock to the southwest. This provided a unique opportunity to contrast geothermal context on either side of the Denali fault. The temperature gradients in KFN-L and DRGW are 45 and 35 ⁰C km-1, respectively. Fibre-optic digital temperature sensing was used to produce high-resolution thermal conductivity profiles for each borehole. These results led to a heat flux estimation of ⁓ 90 mW m-2 at both sites. The field results were then combined into a coupled groundwater flow and heat transfer model to evaluate temperature at depth. This poster presents the evaluation of the geothermal potential around Burwash Landing, considering the influence of the Denali fault on local geothermal resources alongside socio-economic factors. Both the local geology and socio-economic factors are combined to offer Kluane First Nation context-informed recommendations for the integration of geothermal energy into their energy budget.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".