Effective Monitoring of Permafrost Coast Erosion: Wide-scale Storm Impacts on Outer Islands in the Mackenzie Delta Area
Bibliographic record
Abstract
Permafrost coasts are extensive in scale and complex in nature, resulting in particular challenges for understanding how they respond to both long-term shifts in climate and short-term extreme weather events. Taking examples from the Canadian Beaufort Sea coastline characterised by extensive areas of massive ground ice within slump and block failure complexes, we conduct a quantitative analysis of the practical performance of helicopter-based photogrammetry. The results demonstrate that large scale (>1 km2) surface models can be achieved at comparable accuracy to standard UAV surveys, but 36 times faster. Large scale models have greater potential for progressive alignment and contrast issues and so breaking down image sequences into coherent chunks has been found the most effective technique for accurate landscape reconstructions. The approach has subsequently been applied in a responsive acquisition immediately before and after a large storm event and during conditions (wind gusts >50 km hr-1) that would have prohibited UAV data acquisition. Trading lower resolution surface models for large scale coverage and more effective responsive monitoring, the helicopter-based data have been applied to assess storm driven-change across the exposed outer islands of the Mackenzie Delta area for the first time. These data show that the main storm impacts were concentrated on exposed North orientated permafrost cliff sections (particularly low cliffs, >20 m in height) where cliff recession was 43% of annual rates and in places up to 29% of the annual site-wide erosion volume was recorded in this single event. In contrast, the thaw-slump complexes remained relatively unaffected, debris flow fans were generally more resistant to storm erosion than the ice-rich cliffs, perhaps due to the relatively low amounts of precipitation that occurred. Therefore, the variability of permafrost coast erosion rates is controlled by interactions between both the forcing conditions and local response mechanisms. Helicopter-based photogrammetric surveys have the potential to effectively analyse these controls with greater spatial and temporal consistency across more representative scales and resolutions than has previously been achieved, improving the capacity to adequately constrain and ultimately project future Arctic coast sensitivity.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".