A long term synthesis of permafrost ground temperature data along the Alaska Highway, Yukon, Canada
Bibliographic record
Abstract
As a remote territory, the Yukon relies on the Alaska Highway as the only ground transportation route into the territory and Alaska, delivering food and other essential goods, connecting communities, and linking industries to international markets.Within the boundaries of the Yukon, the Alaska Highway is built on a wide variety of landscapes, including sporadic and extensive discontinuous permafrost, which has important impacts on the road conditions as it thaws.The North Alaska Highway, in particular, experiences ongoing permafrost thaw issues.To assess changes in the thermal regime of permafrost along the northern stretch of the Alaska Highway, we use data from the seven longest running ground temperature monitoring sites from Burwash Landing to the Canada-US border from 2013-2022.Ground temperature increased at all sites over the study period except for the second most southern site (BH02).Ground temperature at the northern sites warmed faster than at the southern sites.This difference could be attributed to the presence of greater ice content at the southern sites relative to the northern sites, as well as warmer overall ground temperature regimes arising from more recent deglaciation at the southern sites.Warming air temperatures since the late 1960s are also likely to contribute to a general rise in ground temperature.Higher overall snow deposition in the Beaver Creek region likely contributes to the faster rate of ground temperature increase at the northern sites relative to the southern sites due to the insulating effects of snow.This study provides an important first look at permafrost conditions along the Northern Alaska Highway. 1
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".