Stalking the Forests and Swimming in the Oceans: Trophic Flexibility in Alaskan Coastal Grey Wolves
Bibliographic record
Abstract
Coastal grey wolves of Southeast Alaska (Canis lupus ligoni) are opportunistic apex predators endemic to their environments. Large terrestrial vertebrates such as deer and marine organisms such as salmon dominate their diet. Large terrestrial vertebrates such as deer and marine organisms such as salmon dominate their diet. In recent years, wolves from Gustavus have moved back and forth between the mainland coastal environment and Pleasant Island, a small island 10 kilometers offshore. Wolves initially foraged on the island deer but more recently, a group of wolves has remained full-time on the island to forage on a marine diet including salmon, sea otters, and harbor seals from the nearshore shallow water environment. Stable carbon (δ13C) and nitrogen (δ15N) isotope ratios of the wolf hair and prey demonstrate distinct isotopic division from the herbivorous terrestrial prey (δ13C: -28 to -26‰, δ15N: 0 to 3‰) and the omnivorous marine prey (δ13C: -21 to -18‰, δ15N: 7 to 10‰) to the carnivorous wolf signatures (δ13C: -18 to -12‰, δ15N: 10 to 18‰). This unique trophic behavior in the mountainous coastal environment of Alaska may not be so unique elsewhere in the Pacific Northwest. One thousand kilometers to the south, the British Columbia coastal grey wolf, also known as the Vancouver sea wolf, forages primarily on marine organisms with little to no terrestrial input, though they are abundant in the area. Although these wolves share a similar diet and habitat, these mammals are genetically different. By observing trophic changes in coastal grey wolves, scientists may better understand how environmental fluctuations can influence the opportunistic foraging behavior of these wolves.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".