Thermal behaviour of retrogressive thaw slumps over time revealed by ERT - an example from Herschel Island, Canada
Bibliographic record
Abstract
In the last century the number of retrogressive thaw slumps has doubled in some arctic regions, e.g. Herschel Island, Yukon Territory, Canada [Lantuit and Pollard, 2008]. Retrogressive thaw slumps are a common thermocarst landform along the coast of Herschel Island [Lantuit and Pollard, 2005]. However mechanical conditions leading to the evolution of those retrogressive thaw slumps are poorly understood. For a better understanding of internal thermal processes in these retrogressive thaw slumps we implemented different electrical resistivity profiles (ERT). They cross the focused thaw slump longitudinally and transversally. We compared about 2 km of new ERT-data from 2019 with the same transects from 2011 to gain information about the temperature distribution and the temperature changes in the slump ground. The aim for our study is to gain a profound understanding of the strong and deep thermal disturbances generated by retrogressive thaw slumps and how they change over time, leading to a possible polycyclicality of these slumps. Lantuit, H., and W. H. Pollard (2005), Temporal stereophotogrammetric analysis of retrogressive thaw slumps on Herschel Island, Yukon Territory, Nat. Hazards Earth Syst. Sci. 5 (3), 413–423. Lantuit, H., and W. H. Pollard (2008), Fifty years of coastal erosion and retrogressive thaw slump activity on Herschel Island, southern Beaufort Sea, Yukon Territory, Canada, Geomorphology 95 (1-2), 84–102.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".