A framework to assess permafrost thaw threat for land transportation infrastructure in northern Canada
Bibliographic record
Abstract
Abstract Prediction of permafrost stability is associated with challenges, such as data scarcity and climate uncertainties. Here we present a data-driven framework that predicts permafrost thaw threat based on present ground ice distributions and ground surface temperatures predicted via machine learning. The framework uses long short-term memory models, which account for the sequential nature of climate data, and predicts ground surface temperature based on several climate variables from reanalysis products and regional climate models. Permafrost thaw threat is then assessed for three cases in northern Canada: Hudson Bay Railway, Mackenzie Northern Railway, and Inuvik–Tuktoyaktuk Highway. The models predict ground surface warming in all studied areas under both moderate and extreme climate change scenarios. The results also suggest that all studied cases are already under threat, with the northern sections of the Hudson Bay Railway and Inuvik–Tuktoyaktuk Highway facing an increasing threat by the end of the century.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".