Effect of snow cover on the vulnerability of lemmings to mammalian predators in the Canadian Arctic
Bibliographic record
Abstract
In the Arctic tundra, snow is believed to protect lemmings from mammalian predators during winter. We hypothesized that snow quality (depth and hardness) should affect mammalian predation rates on lemmings, but that this effect would depend on the predator hunting strategy; and that predation by ermines (Mustela erminea), which can hunt lemmings under the snow, should be higher in preferred lemming habitats. We measured snow depth and hardness at tunnels made by arctic fox (Vulpes lagopus) predation attempts, at winter nests nonpredated and predated by ermines, and at random locations. We also determined winter nest density in 3 habitats (wet, mesic, and gully). Deep and hard snow restricted fox predation attempts made by jumping through the snow, but not those made by digging. Ermine predation was unaffected by snow depth and weakly by nest density but was higher in gully and intermediate in mesic habitats, which are conducive to high snow accumulation, compared to the wet habitat. These results indicate that habitat-related topographical features are more important than snow depth or nest density per se in affecting the winter foraging strategy of ermines. Overall, even though we found a relatively weak effect of the snow cover on predation by foxes and ermines, it is difficult to predict how upcoming changes to the snow cover will affect lemming vulnerability to mammalian predators because a wide range of snow conditions may result from climate warming.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".