Combining ERT and an orthophoto time series to investigate thaw-related landslides in the Canadian Arctic
Bibliographic record
Abstract
Retrogressive thaw slumps (RTS) are a common permafrost related landslide type in the Arctic and provide a large amount of material to coastal nearshore zone, lakes and rivers. RTS are characterized by highly dynamic changes and rapid internal processes. Along the Canadian coastline there is an increasing number of RTS documented over the last century, acting sensitive to a warming climate.The occurrence and behaviour of these landslides is strongly dependent on the presence of ground ice, including their likelihood for polycyclicity and reactivation. To detect and evaluate the ground ice content in different activity- and stabilization stages we used electrical resistivity tomography (ERT) on several RTS on Herschel Island in the Canadian Beaufort Sea. We combined ERT profiles remeasured 10 years apart, with orthophotos since 1952 to gain a detailed insight in their long-term behaviour, the availability of ground ice and the factors controlling polycyclicity.This study demonstrates the capacity of ERT for detecting massive ice bodies and internal changes. Combining this with a time component and orthophoto analysis, provides a unique insight into the behaviour of retrogressive thaw slumps, but also shows the need to use complimentary techniques to correctly interpret geophysical measurements.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| 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".