Foraging Behaviours of Wolverines at a Large Arctic Goose Colony
Bibliographic record
Abstract
At the large Ross's goose and lesser snow goose colony at Karrak Lake, Nunavut, Canada, we saw wolverines kill two geese, take 13 eggs from 12 goose nests, and take three goose carcasses from two fox dens. Wolverines also made unsuccessful attempts to capture geese and frequently ignored eggs from nests where geese had fled the approaching wolverine. Most foods (all geese killed by wolverines and 80% of the eggs) were cached for later use, whereas few foods were eaten immediately (20% of the eggs and part of a goose taken from a fox den, which was later lost) or lost (all geese taken from fox dens). Wolverines spent little time caching foods (e.g., some foods were never covered), which suggests that recovery of these foods was not crucial to wolverines. When taking foods from fox dens, wolverines were mobbed by foxes; as a result, only one wolverine managed to consume part of a goose carcass taken from a fox den. These observations illustrate the opportunistic nature of wolverines and suggest that their scavenging success may be influenced by how well foods are defended.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".