Simulating Active Layer Thaw in a Boreal Environment
Bibliographic record
Abstract
A large part of the boreal zone of the western Canadian Arctic is underlain by ice-rich discontinuous permafrost which when thawed, can lead to settlement of the ground surface that has implications for the integrity of northern infrastructure, including oil and gas pipelines. A simple yet physically-based model is desired to simulate thawing of the active layer in different materials commonly found along the Mackenzie Valley pipeline corridor. Stefan’s algorithm determines the phase change of soil moisture using ground surface temperature as the upper boundary condition and conduction to transfer heat to the freeze-thaw front. It is tested on a permafrost site near Wrigley, Northwest Territories, where the computed thaw penetration compares satisfactorily with field data. To further explore the effects of climate and soil types on active layer depth, three representative sites in the Mackenzie valley where ground surface temperatures are available were selected for simulation of ground thaw, under two summer conditions. Results of the simulation demonstrate the sensitivity of active layer thaw to (1) soil materials due to differential thermal properties, (2) moisture content, which largely controls the latent heat requirement for phase change, and (3) inter-annual variations in ground surface temperature. Given the strong potential for environmental changes in the vast boreal region, the model allows the active layer thaw responses to be easily assessed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".