Exploring the diet of arctic wolves (<i>Canis</i> <i>lupus</i> <i>arctos</i>) at their northern range limit
Bibliographic record
Abstract
The grey wolf (Canis lupus Linnaeus, 1758) is one of the most widespread large carnivores on Earth, and occurs throughout the Arctic. Although wolf diet is well studied, we have scant information from high Arctic areas. Global warming is expected to increase the importance of predation for ecosystem regulation in Arctic environments. To improve our ability to manage Arctic ecosystems under environmental change, we therefore need knowledge about Arctic predator diets. Prey remains in 54 wolf scats collected at three sites in the high Arctic region surrounding the Hall Basin (Judge Daly Promontory, Ellesmere Island, Canada, and Washington Land and Hall Land, both in northwestern Greenland) pointed to a dietary importance of arctic hare (Lepus arcticus Ross, 1819; 55% frequency of occurrence) and muskoxen (Ovibos moschatus (Zimmermann, 1780); 39% frequency of occurrence), although we observed diet variation among the sites. A literature compilation suggested that arctic wolves (Canis lupus arctos Pocock, 1935) preferentially feed on caribou (Rangifer tarandus (Linnaeus, 1758)) and muskoxen, but can sustain themselves on arctic hares and Greenland collared lemmings (Dicrostonyx groenlandicus (Traill, 1823)) in areas with limited or no ungulate populations. We suggest that climate change may alter the dynamics among wolves, arctic hare, muskoxen, and caribou, and we encourage further studies evaluating how climate change influences predator–prey interactions in high Arctic environments.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".