Prolonging the arctic pulse: long‐term exploitation of cached eggs by arctic foxes when lemmings are scarce
Bibliographic record
Abstract
1. Many ecosystems are characterized by pulses of dramatically higher than normal levels of foods (pulsed resources) to which animals often respond by caching foods for future use. However, the extent to which animals use cached foods and how this varies in relation to fluctuations in other foods is poorly understood in most animals. 2. Arctic foxes Alopex lagopus (L.) cache thousands of eggs annually at large goose colonies where eggs are often superabundant during the nesting period by geese. We estimated the contribution of cached eggs to arctic fox diets in spring and autumn, when geese were not present in the study area, by comparing stable isotope ratios (delta(13)C and delta(15)N) of fox tissues with those of their foods using a multisource mixing model in Program IsoSource. 3. The contribution of cached eggs to arctic fox diets was inversely related to collared lemming Dicrostonyx groenlandicus (Traill) abundance; the contribution of cached eggs to overall fox diets increased from < 28% in years when collared lemmings were abundant to 30-74% in years when collared lemmings were scarce. 4. Further, arctic foxes used cached eggs well into the following spring (almost 1 year after eggs were acquired) - a pattern that differs from that of carnivores generally storing foods for only a few days before consumption. 5. This study showed that long-term use of eggs that were cached when geese were superabundant at the colony in summer varied with fluctuations in collared lemming abundance (a key component in arctic fox diets throughout most of their range) and suggests that cached eggs functioned as a buffer when collared lemmings were scarce.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| 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 teacher head, 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".