Advancing snow modelling across Canada from the Arctic to southern regions
Bibliographic record
Abstract
The climate is changing rapidly in the Canadian Arctic and in southern regions in Canada such as the Great Lakes. We adapted the SNOWPACK model from traditional avalanche applications for the Canadian Artic and for two southern regions with moderate (Bay of Quinte, Ontario) to low snowfall (Wigle Creek, Ontario). We developed innovative tools to process large meteorological forcing data and to spatialize output. We also developed a downscaling tool (Outil de Spatialisation de SNOWPACK pour l’Arctique - OSSA) using changes in slope, which refined the spatial resolution of simulations by 45-fold. Our simulations in the Arctic demonstrated that icing events tripled across the Canadian Arctic Archipelago between 1979-2011. SNOWPACK simulations (1970s to 2020) for the Bay of Quinte focused on changes in snow parameters such as Snow Water Equivalent (SWE), which drives snow melt and flooding. Other parameters such as snow density will also be discussed. Simulations show a substantial change in SWE, especially after 2000. In the region with low snowfall (Wigle Creek), simulations of snow on and off will be presented.We will also illustrate how we advanced SNOWPACK model validation standards though a multi-pronged approach: 1) remote sensing data to validate snow spatial extent, 2) field measurements with sensors to quantify soil temperature feedback, 3) traditional snow pits to validate SWE, and 4) drones to measure snow height and SWE. Finally, we show that validation standards should be adapted to each region based on snowfall and snowmelt.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".