Geothermal favourability in data-scarce regions: incorporating physical and socio-economic factors into a modified Play fairway approach, southwestern Yukon, Canada
Bibliographic record
Abstract
Geothermal energy could be used to reduce or replace diesel for heating in remote northern communities. Geothermal development has primarily focused on shallow, high-temperature resources, but interest in low-temperature and deep geothermal resource exploration has increased as energy costs and climate change policy have evolved. Here, we evaluated the low-temperature geothermal favourability in southwestern Yukon by adapting Play fairway analysis to data-scarce regions. Play fairway analysis is a spatial statistical tool that uses a layered data approach to model favourability and risk assessments for resource exploration. Previous Play fairway analyses concentrate on the physical aspects of geothermal favourability: heat, permeability, and fluid availability. This study presents an overview of potential direct and indirect physical parameters that could be used in a geothermal Play fairway analysis in data-scarce regions and introduces the importance of considering socio-economic data in the exploration phase. The socio-economic controls are grouped into quantitative and qualitative parameters that describe population trends and community interests. The framework presented is then applied to a Play fairway analysis for southwestern Yukon. Based on the physical and socio-economic analysis, there is interest in exploring geothermal potential along the Denali fault near Duke River to support the community of Burwash Landing. Supplementary Information: The online version contains supplementary material available at 10.1186/s40517-025-00345-6.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".