Geothermal Exploration in the Burwash Landing Region, Canada, Using Three-Dimensional Inversion of Passive Electromagnetic Data
Bibliographic record
Abstract
Sustainable development of Canada’s North requires an increased focus on renewable, zero-emission energy sources. Burwash Landing in Yukon is prospective for geothermal energy based on a high geothermal gradient, local occurrence of warm groundwater and proximity to the active, crustal-scale Denali fault. Uncertainties about the potential geothermal system include the nature and geometry of fluid pathways, and heat sources required to drive a hydrothermal system. In this study, we inverted three passive electromagnetic datasets—321 extremely low frequency electromagnetic, 33 audiomagnetotelluric and 51 magnetotelluric stations—to map the subsurface electrical structure to 8 km depth. Our new model reveals vertical conductive structures associated with the two main faults, Denali and Bock’s Creek, which we interpret to represent fluid-deposited graphite and hydrothermal alteration, respectively. Our model supports an interpreted releasing bend on the main Denali fault strand. This is associated with the deepest conductivity anomaly along the fault and potential for deeper penetration of fluids. Enigmatic conductive bodies from 1 to > 6 km depth are associated with intermediate to mafic intrusions. Fluids released from these bodies may advect heat and provide a possible heat source to mobilize hot fluids and sustain a geothermal system in the region.
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 | 0.000 | 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 | 0.001 | 0.000 |
| 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".