A-ERT Insights: Linking resistivity, temperature, and moisture dynamics in two distinctive Antarctic permafrost lithologies
Bibliographic record
Abstract
This study investigates the relationship between electrical resistivity, ground temperature, and soil moisture across two contrasting lithologies within the permafrost environment on James Ross Island, located in the north-eastern Antarctic Peninsula. The region is characterized by continuous permafrost and a semi-arid polar continental climate with a mean annual air temperature of approximately -7 °C. The monitoring transect crosses a lithological boundary between a Holocene marine terrace and finer-grained Cretaceous sediments. An automated electrical resistivity tomography (A-ERT) system, utilizing a 4POINTLIGHT_10W (Lippmann) device, was installed near the Czech Antarctic station Johann Gregor Mendel in February 2023. The system performs daily resistivity measurements along a 23-metre transect with 47 electrodes spaced 0.5 m apart, probing depths up to 4.5 meters. Complementary temperature sensors (placed at depths from 5 to 200 cm) and soil moisture sensors (at 5, 35, 55, and 75 cm) provide additional context on thermal and moisture regimes within each of the distinctive lithologies.Approximately two years of data reveal significant lithology-dependent variations in resistivity. Resistivity values are consistently higher in the coarser-grained Holocene marine terrace than in the finer-grained Cretaceous sediments. Overall, resistivity increases rapidly during winter (approximately 1–2 kΩm) and decreases during the thawing phase (approximately 10–100 Ωm), closely following the changes in ground temperature and soil moisture. The thaw front progression is readily observable in resistivity data, highlighting contrasting thermal and hydrological responses between lithologies. These relationships also form the basis for modelling ground temperature across the whole transect using resistivity data, offering a predictive approach to understanding permafrost dynamics.This study demonstrates that A-ERT provides robust, high-resolution insights into the interplay between lithology, thermal regime and soil moisture in permafrost environments, surpassing the spatial limitations of traditional borehole methods and enabling effective long-term monitoring in extreme Antarctic conditions.
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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 | 0.001 |
| 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) | 0.001 | 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".