Bibliographic record
Abstract
Terrestrial predators in coastal areas are often subsidized by marine foods. In order to determine the potential impact on terrestrial prey, the numerical response of predators to each food source must be determined. In winter, arctic foxes (Alopex lagopus) may forage on the frozen Arctic ocean and scavenge carcasses of seals killed by polar bears (Ursus maritimus), but the importance of this food source and its effect on the population cycles of arctic foxes and lemmings (their primary prey) are unclear. I estimated the marine component of the late winter diet of arctic foxes near Churchill, Manitoba, using stable-carbon isotope analysis, and compared these estimates to abundance of arctic foxes and collared lemmings (Dicrostonyx richardsoni). From 1994 to 1997, fox density varied with lemming abundance, but following a decline, fox abundance began increasing before lemmings. During this increase marine foods were consumed more than in other years, with over two-thirds of food intake from marine sources. Arctic and red fox (Vulpes vulpes) harvests in the 1980s to 1990s were correlated with published estimates of polar bear body mass, which varies with seal productivity. However, this relationship disappeared during high lemming years. Thus, variation in marine productivity affects arctic fox abundance, especially when their primary prey are scarce, and this numerical response of arctic foxes to marine resources and lemmings suggests that increased predation by arctic foxes subsidized by seal carrion may delay the recovery of low lemming populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".