Comparison of ground-based and airborne transient electromagnetic methods for mapping glacial and permafrost environments: Cases from McMurdo Dry Valleys, Antarctica
Bibliographic record
Abstract
The transient electromagnetic (TEM) method is a non-invasive geophysical tool well-suited for subsurface imaging in cold and polar regions, where common targets are associated with strong contrasts in electrical resistivity. By imaging the electrical properties of the subsurface, the TEM methods can discriminate between geological units such as frozen ground (permafrost), fresh/saline groundwater systems, and bedrock/glacier ice. In this study, we compare TEM data acquired with ground-based and airborne TEM systems. We demonstrate the mapping capabilities of these two approaches in high latitude polar environments with datasets from Taylor Glacier, Lake Vanda, and Canada Glacier in the McMurdo Dry Valleys of Antarctica. The results show a high consistency between the airborne and ground-based TEM data, both with a high resolution and a deep penetration depth down to hundreds of meters due to the resistive background material, which makes both approaches capable of mapping hydrological systems and identifying the base of glaciers. The airborne TEM approach offers an unmatched spatial data coverage in difficult terrain and a far improved lateral resolution compared to the static ground-based system. The ground-based TEM system offers the possibility for using larger transmitter coils and longer stacking times and therefore has potential for reaching deeper penetration depths. The ground-based TEM approach is hence a valuable tool that can provide consistent imaging results while also being far more accessible in terms of cost and field logistics compared to an airborne TEM campaign.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".