Heat flux evaluation based on active fiber optic distributed temperature sensing tests in southwestern Yukon, Canada
Bibliographic record
Abstract
Geothermal energy could decrease the dependence of remote regions on diesel by offering an alternative baseload energy. However the geothermal exploration risk is high in these areas due to limited temperature and ground thermal conductivity data, and resultant heat flux evaluations. Thermal response tests are commonly used in the heat pump industry to evaluate the effective thermal conductivity; however these tests are typically performed in shallow wells (< 200 m) and assume the effective thermal conductivity to be purely due to conduction, but groundwater flow is known to have an influence on effective thermal conductivity. Here, we present a method to produce a high-resolution in-situ effective thermal conductivity profile using fibre-optic distributed temperature sensing during active thermal response tests. The high-resolution profiles coupled with the concept of effective thermal conductivity under the influence of groundwater flow can be used to isolate conduction-dominated segments of temperature and effective thermal conductivity profiles. This method was applied to two boreholes in southwestern Yukon on the traditional territory of Kluane First Nation (KFN-L: 387 m and DRGW: 220 m). The heat flux was evaluated based on conductive segments of the temperature and thermal conductivity profiles. The temperature profile was corrected for topography and paleoclimate effects, and the internal heat generation was also considered. This resulted in heat flux estimation of 89 mW m-2 and 99 mWm-2 at KFN-L and DRGW, respectively. These values decrease exploration uncertainty around Burwash Landing, Yukon, where data scarcity previously increased exploration risk. This method could be applied to confidently estimate local terrestrial heat flux in pre-existing moderate to deep boreholes (up to 1 km).
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".