The Role of Massive Ice and Exposed Headwall Properties on Retrogressive Thaw Slump Activity
Bibliographic record
Abstract
Abstract Retrogressive Thaw Slumps (RTSs), a highly dynamic form of mass wasting, are accelerating geomorphic change across ice‐cored permafrost terrain, yet the main controls on their activity are poorly constrained. Questions over the spatial variability of environmentally sensitive massive ice bodies and a paucity of high‐spatial and temporal resolution topographic data have limited our ability to project their development and wider impacts. This research addresses these key problems by investigating RTS processes on Peninsula Point—a well‐studied site for intra‐sedimental massive ice in the Western Canadian Arctic. Utilizing high‐resolution topographic data from drone surveys in 2016, 2017 and 2018 we (a) measure the temporal and spatial variations in headwall properties and retreat rates, (b) determine the spatial pattern of subsurface layering using passive seismic monitoring and (c) combine these to analyze and contextualize the factors controlling headwall retreat (HWR) rates. We find that headwall properties, namely massive ice and overburden thickness, are significant controls over rates of HWR. Where persistent massive ice exposures are present inland of the headwall, regardless of thickness, and overburden thickness remains <4 m, HWR is typically more than double that of other headwalls. Furthermore, a 3D site model was created by combining photogrammetric and passive seismic data, highlighting internal layering variability and demonstrating the limitations of extrapolations of internal layering based on headwall exposures. These results provide fresh insights into the in situ controls on HWR rates and new approaches to understanding their variability.
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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 | 0.000 |
| 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".