Surviving on cached foods — the energetics of egg-caching by arctic foxes
Bibliographic record
Abstract
Food-caching by arctic foxes ( Vulpes lagopus (L., 1758)) is a behavioural adaptation thought to increase winter survival, especially in bird colonies where a large number of eggs can be cached during a short nesting season. In this paper, we measured the energy content of greater snow goose ( Chen caerulescens atlantica Kennard, 1927) eggs and evaluated their perishability when cached in tundra soil for a whole summer. We estimated that eggs lost only ~8% of their dry mass over 60 days of storage in the ground. We used published estimates on digestibility of nutrients by arctic foxes to estimate that fresh and stored goose eggs contained 816 and 730 kJ of metabolizable energy, respectively, a difference of 11%. Using information on arctic fox energetics, we evaluated that 145 stored eggs were required to sustain the growth of one pup from the age of 1 to 3 months (nutritional independence). Moreover, 23 stored eggs were energetically equivalent to the average fat deposit of an arctic fox during winter. Finally, we calculated that an adult arctic fox would need to recover 160–220 stored eggs to survive 6 months in resting conditions during cold winter temperatures. This value increased to 480 when considering activity cost. Based on egg acquisition and caching rates observed in many goose colonies, we conclude that cached eggs represent an important source of energy relative to the needs of an arctic fox during winter, and have thus a high fitness value.
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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 | 0.000 | 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".