Foraging patterns of arctic foxes at a large Arctic goose colony. Arctic 53(3
Bibliographic record
Abstract
ABSTRACT. Arctic foxes (Alopex lagopus) are the main predators of many arctic-nesting birds, and such predation can have a large impact on the nesting performance of geese in some years and in some parts of the Arctic. We examined foraging patterns of arctic foxes at a large lesser snow goose (Chen caerulescens caerulescens) colony on Banks Island, Canada, from 1996 to 1998 and were especially interested in the proportion of food that was cached for later use and the impact that fox predation had on goose productivity. Arctic foxes took mostly eggs when foraging among geese, and most of these eggs (97%) were cached for later use. Adult geese and lemmings were taken in low numbers, and most of these foods (83 % of geese and 75 % of lemmings) were eaten immediately. In years with high fox abundance, the foxes spent considerable effort moving eggs from old caches. This behaviour may have resulted from high rates of cache pilfering, or foxes may have been moving caches to deter cache pilfering. The impact of fox predation was low in all years, and foxes took only about 4 –8 % of all eggs available at the colony during incubation each year. However, caching and use of cached eggs may influence the survival of arctic foxes by forming significant parts of their winter diet or by supplementing the diets of growing young: during nesting each year, foxes took on average 900 –1570 eggs per fox.
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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 | 0.000 |
| 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".