Geophysical mapping of ground ice using a combination of capacitive coupled resistivity and ground‐penetrating radar, Northwest Territories, Canada
Bibliographic record
Abstract
The nature and distribution of ground ice are two of the most unpredictable geological variables in near‐surface deposits characterized by continuous permafrost. Subsurface information about ground ice distribution and structure can be obtained either by invasive and environmentally destructive techniques like drilling and excavation or by noninvasive low‐impact geophysical methods. In this study, coordinated measurements by two complementary geophysical tools, capacitive‐coupled resistivity (CCR) and ground‐penetrating radar (GPR) were used to map ground ice in a variety of locations in the Mackenzie Delta region of the western Canadian Arctic. Both CCR and GPR systems are highly portable (especially on snow covered surfaces) and very effective in collecting data under winter conditions when cold ground temperatures ensure that nearly all liquid water is frozen and signal penetration is enhanced. CCR and GPR readily detect stratigraphic differences including the contacts between massive ice deposits and enclosing sediments. GPR is widely used in permafrost research, but CCR has been used in only a few studies. This is the first study to combine results from both systems by collecting complementary data sets along coincident transects. We demonstrate that when combined, these data increase the quality and interpretation of subsurface information beyond what could be determined by either of the instruments alone. The complementary nature of these two geophysical tools facilitated the detection and mapping of massive ground ice, ice‐rich sediments, ice wedges, thermokarst, and basic stratigraphic relationships. This study breaks new ground by documenting the benefits of using these techniques together in permafrost investigations.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".