Influence of Degrading Permafrost on Landsliding Processes: Little Salmon Lake, Yukon Territory, Canada
Bibliographic record
Abstract
A landslide inventory was carried out for the Little Salmon Lake area, Yukon Territory, Canada, in response to observations of several new landslides in the area, suspected to be the result of degrading permafrost. The largest of these landslides, the Magundy River bi-modal flow-slide, has progressed over the last decade until it now involves over 1x10 m of material. The inventory is based on terrain mapping and field work, and includes multiple landslide types. The field work provided the opportunity to examine the slides, ground truth the map, and to examine the progression of the landslide, as well as the massive ground ice exposed in the scarps of the currently active slides. Permafrost degradation can be driven by anthropogenic or natural agents of change. The study investigated natural agents of change, as anthropogenic sources are not active, due to the remote and undeveloped nature of the area. Temperature data from the area indicates a warming trend of 3oC over the last 40 years, supporting the theory that climate amelioration is one of the major factors generating the new activation of landslides in the area. Susceptibility maps were developed to examine the potential for landslide initiation due to permafrost degradation. The most important data required for this work is the distribution of ground ice. In the absence of any borehole or geophysical data in the area, or generally of detailed mapping of permafrost distribution in the Canadian north, an expert system was used to predict the location of ground ice. Therefore, the landslide susceptibility maps are very dependent on the accuracy of this map. Should development in the valley proceed, more accurate landslide susceptibility mapping would be required. Due to the importance of the ground ice distribution and condition, it would be recommended that data be collected to accurately map the ice and therefore to improve the accuracy of the prediction of possible landslides.
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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 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".