Comparison of Automated ERT stations (A-ERT) for continuous monitoring electrical resistivity in polar and mountain permafrost regions
Bibliographic record
Abstract
Permafrost is warming globally as shown in many recent studies based on borehole temperature monitoring. However, data on changes in ground ice and water content in permafrost areas are scarce, which are both expected to change strongly close to the melting point when latent heat effects upon melting mask further temperature increase until all ice has melted. This is the reason why permafrost borehole temperature monitoring is in many cases complemented by geophysical surveying, such as Electrical Resistivity Tomography (ERT), due to the strong dependence of electrical resistivity on liquid water content. ERT has been successfully applied to e.g. spatially map the active layer depth, quantify ice and water content and detect and delineate massive ice bodies within the permafrost since many years. In several cases survey lines were repeated or monitored over short time-periods to identify freeze-thaw processes or identify permafrost changes over longer time periods. However, only very rarely electrical resistivity is monitored operationally by an automated station.In recent years, automated ERT (A-ERT) systems have been specifically developed to be deployed in harsh and remote terrain, and several systems have been installed in permafrost environments within different research projects. In this study, we collect and compare first results from several of these A-ERT stations regarding data quality over a full year monitoring period, specifics of current injection and contact resistances, energy consumption and resistivity evolution over freeze and thaw cycles. The continuously monitored permafrost resistivity data are compared for several A-ERT stations in polar and mountain regions, including the Antarctic Peninsula Region, Yukon and the Northwest Territories, Svalbard, Kyrgyzstan, Greenland and the European Alps. Finally, we will present processing approaches to relate the obtained resistivity changes to changes in water content and compare them to in-situ measured temperature and soil moisture data.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".