Spatialization of the SNOWPACK snow model for the Canadian Arctic to assess Peary caribou winter grazing conditions
Bibliographic record
Abstract
Peary caribou is the northernmost designatable unit for caribou species, and its population has declined by about 70% over the last three generations. The Committee on the Status of Endangered Wildlife in Canada identified difficult grazing conditions through the snow cover as being the most significant factor contributing to this decline. This study focuses on a spatially explicit assessment tool using snow model simulations (Swiss SNOWPACK model driven in an off-line mode by spatialized meteorological forcing data generated by the Canadian Regional Climate Model) to characterize snow conditions for Peary caribou grazing in the Canadian Arctic. The life cycle of Peary caribou has been subdivided into three critical periods: summer foraging and fall breeding (July–October), winter foraging (November–March), and spring calving (April–June). Winter snow conditions are analyzed and snow simulations compared to Peary caribou island counts to identify a snow parameter that could potentially act as a proxy for grazing conditions and explain fluctuations in Peary caribou numbers. This analysis concludes that caribou counts are affected by simulated snow density values >300 kg m−3. A software tool mapping possibly favorable and unfavorable grazing conditions based on snow is proposed at a regional scale across the Canadian Arctic Archipelago. Specific output examples are given to show the utility of the tool, mapping pixels with cumulative snow thickness above densities of 300 kg m−3, where cumulative seasonal thicknesses >7000 cm are considered unfavorable.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".