Active-layer detachment failures and the vulnerability of infrastructure in Alaska and northwestern Canada
Bibliographic record
Abstract
As the Arctic warms at nearly four times the global average, the thawing of ice-rich permafrost is destabilizing the ground and amplifying thermokarst-related mass-wasting events, including active-layer detachment failures (ALDs). ALDs are translational landslides that occur during summer thaw and are very common across the Arctic in both continuous and discontinuous permafrost areas, most typically in ice-rich unconsolidated sediments. These events are becoming increasingly common and pose significant risks to the region's topography, vegetation, hydrology, infrastructure integrity, and carbon exchange. This study investigates the susceptibility of ALDs in permafrost regions under current climate conditions, with a particular focus on Alaska and the Northwest Territories of Canada. Using the Maxent statistical model, we developed a susceptibility map for ALDs across the study area, providing valuable insights into the spatial distribution of ALD-prone zones. Our analysis revealed high-susceptibility regions in critical areas, including the Brooks Range, Franklin Mountains, and West Crazy Mountains in Alaska, as well as the areas around Dawson City and Mackenzie River regions in Canada. A particular concern is the vulnerability of linear infrastructure, with significant portions (39% in total) of roads and pipelines located in high to very high susceptibility zones. These findings underscore the broader implications of climate change in the Arctic regions, particularly the destabilization of permafrost. They highlight the necessity of adapting infrastructure and management strategies to mitigate the growing risks associated with ALD events.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".