Active Layer Variability and Permafrost Stability on Axel Heiberg Island: Comparing Electrical Resistivity Tomography and Ground-Penetrating Radar
Bibliographic record
Abstract
High Arctic landscapes are undergoing rapid change, warming at least four times faster than the global average. Because these regions are dominated by continuous permafrost, thawing leads to deeper active layers (AL) and increasing groundwater infiltration, altering hydrology and mobilizing nutrients and metals. This study was designed to compare the effectiveness of electrical resistivity tomography (ERT) and ground-penetrating radar (GPR) for characterizing permafrost environments and capturing short-term thaw dynamics. Fieldwork took place on a west-facing slope at Expedition Fiord, Axel Heiberg Island, where two 20 m transects (NShill and WEhill) were surveyed repeatedly over five days (July 23–27, 2025). Surveys used ERT (Wenner and dipole-dipole arrays), GPR (500 MHz), permafrost probing, and soil probes (moisture, temperature, conductivity). The original aim was to evaluate how the two geophysical methods compare, while testing the hypothesis that AL properties respond strongly to weather variability, whereas permafrost (PF) remains relatively stable. Results confirm that the AL is highly sensitive to both weather and microtopography, while PF resistivity remained stable within orders of magnitude over the study period. Soil data showed persistent wet anomalies at NShill (electrodes 10–12), while WEhill responded more directly to rainfall and drying. ERT consistently resolved a conductive AL above resistive PF, with saturation beneath puddles producing reduced resistivity and stronger attenuation. GPR showed a strong reflective band at ~3–7 ns (AL boundary) and signal loss below ~10–12 ns, especially at saturated troughs. Across methods, AL thickness followed the relation probe ≤ GPR ≤ ERT, reflecting that each technique senses different physical boundaries. Together, the datasets demonstrate that ERT and GPR are complementary tools: ERT resolves the boundary between active layer and permafrost, while GPR highlights finer-scale thaw patterns and moisture variability near the surface.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".