Influence of Variable Snow Cover Depth and Duration on Model Ground Temperatures in the Eastern Canadian Arctic
Bibliographic record
Abstract
Predictive ground temperature modelling at permafrost sites rely on calibrated computational models that are inherently dependent on all inputs, including climate data. In a perfect case, a local weather station that measures all inputs required to calculate the surface boundary condition are readily available including air temperature, wind speed, relative humidity, albedo, vegetation thickness, solar radiation, and snow depth. However, if one or more of these inputs are not measured, then assumptions are often, if not always, required. Snow cover, in particular, is one such input for which assumptions are required in many cases. At elevated sites in the eastern Canadian Arctic the ground is covered by snow for most, and occasionally all, of the year which increases albedo and insulates the ground against seasonal heat flux. In this paper the effect of four snow cover functions is examined on near-surface and deeper ground temperatures over a six-year model period. The results show that the snow depth and cover duration play a significant role in the modelled ground temperatures. If snow remains year-round, ground temperatures remain below zero throughout the modeled profile. With zero snow cover, near-surface ground temperatures vary widely in accordance with the uncovered surface boundary. Between the two extremes the maximum ground temperature profiles are similar to the ‘no snow’ scenario, while minimum ground temperatures vary based on the depth and duration of snow cover. The results show snow cover assumptions significantly affect temporal and spatial variation of ground temperatures and need to be carefully selected for predictive modelling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| 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".