Susceptibility of active-layer detachment failures and vulnerability of infrastructure in Alaska and northwestern Canada
Bibliographic record
Abstract
Abstract Ongoing climate change is critically endangering cold regions, with the Arctic warming at nearly four times the global average. This rapid warming is not only accelerating the irreversible thawing of permafrost but is also reshaping the region’s topography, vegetation, hydrology, infrastructure integrity, and carbon exchange. The destabilization of the ground through thaw of ice-rich permafrost, known as thermokarst, is increasing to mass-wasting events such as active-layer detachment failures (ALDs), shallow landslides that are becoming increasingly common in the Arctic. In light of these alarming developments, our study employs the Maxent statistical model to analyze ALD distribution, develop a susceptibility map for Alaska and Northwest Territories, Canada, in the current climate, and assess the potential impact to infrastructure. We identified high-susceptibility zones across critical regions, including the Brooks Range, Franklin Mountains, and West Crazy Mountains in Alaska, as well as the Dawson City and Mackenzie River areas in Canada. Particularly concerning is the vulnerability of linear infrastructure: 878 km of roads, 167 km of the Trans-Alaska pipeline, and 140 km of the Norman Wells pipeline are situated in areas of high to very high susceptibility to ALDs. These results highlight the urgent need for proactive strategies and infrastructure planning to deal with the growing threats from permafrost thaw and its wide-ranging effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".