Detection of ice wedges in Yedoma along the Dalton Highway, Alaska, USA, using capacitive-coupled electrical resistivity tomography
Bibliographic record
Abstract
In the 2000s, the Alaska Department of Transportation investigated the possibility of realigning about 5 km of the Dalton Highway in Alaska, USA, between the Mile Posts 8 and 12, to meet current design standards and provide safer alignments and grades than the current ones.The project area is in the continuous permafrost zone where ice-rich syngenetic permafrost with large ice wedges, known as Yedoma, formed during the late Pleistocene.To achieve a geotechnical investigation for assessing the permafrost conditions along the proposed realignment, a total of 136 boreholes were drilled.The ground truth coming from these boreholes offers a unique opportunity to assess the capabilities of engineering geophysical investigation in delineating potentially problematic ice-rich permafrost.Therefore, in addition to this geotechnical investigation, ground penetrating radar (GPR) profiling, direct-current and capacitive-coupled electrical resistivity tomographies (DC-ERT and CC-ERT, respectively) were carried out along the proposed realignment.The permafrost table and top of ice-rich syngenetic permafrost were identified in the GPR profile.Both the higher sensitivity and spatial sampling of CC-ERT than DC-ERT allowed the individual detection of resistive ice wedges embedded in more conductive frozen silts in the model of electrical resistivity from the CC-ERT inversion.This is made possible by the high electrical resistivity contrast between ice wedges and frozen silts near 0 °C.In the DC-ERT model, only large zones of resistive ice-rich permafrost were delineated without individually detecting the ice wedges.This case study shows the capabilities of CC-ERT to delineate ice wedges in a warm permafrost environment.Geophysical investigations prior to geotechnical investigations can help in designing cost-effective drilling campaigns with fewer expensive boreholes for ground truth along planned linear infrastructures in permafrost environments.1
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".