What Triggers Retrogressive Thaw Slumps in the Arctic Coastal Zone?
Bibliographic record
Abstract
Retrogressive thaw slumps (RTS) are spectacular lateral thermokarst features occurring in ice-rich permafrost regions. They develop along streams or coastlines and expand inland to form landslide-like U-shaped scars exceeding a kilometer size in selected locations. These slumps are a major source of ediment, organic carbon and nutrients that have a large effect on the aquatic environment. The consequences of the occurrence of RTS, which have been shown to occur at increased frequencies in the Arctic are not well understood. The impact of RTS sediment delivery on coastal ecosystems is even less known, even though RTS contribute quantities of sediment sometimes greater than coastal erosion itself. In this study, we present the results of a systemic multidisciplinary study attempting to understand the structure, the evolution and the fate of RTS on Herschel Island, Yukon Territory, in the southern Canadian Beaufort Sea and to relate it to slump occurrence. We use information stemming from cryostratigraphic sampling in the ice headwall of the RTS, from cores collected above the headwall and in the slump floor and from geophysics (mostly Direct Current and Capacitive Coupled Resistivity) to describe the structure of the slump. We emphasize the role of ground ice distribution, sea water vicinity and sensible and radiative heat input in dictating the pace at which slump initiate, stabilize and re-activate. We compare this information to past knowledge on slumps to highlight the transient nature of slump occurrence in the arctic coastal zone and the existence of “pulses” of slump activity with potentially important impacts on the nearshore ecosystem.
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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 | 0.001 | 0.001 |
| 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".