Survival rate of experimental food caches: implications for arctic foxes
Bibliographic record
Abstract
Avoiding cache loss is critical to food-hoarding animals. Arctic foxes ( Alopex lagopus (L., 1758)) scatter-hoard thousands of eggs annually at large goose colonies, and we examined how survival rate of experimental caches were influenced by (i) nesting density by geese, (ii) relative proportion of two sympatric goose species, (iii) departure by ca. 1 million geese and their young after hatch, and (iv) age of cache sites. Survival rate of experimental caches was related to age of cache sites in the 1st year of the study (0.80 and 0.56 per 18-day period for caches from new and 1-month-old cache sites, respectively) and departure by geese in the 2nd year of the study (0.98 and 0.74 per 18-day period during and after goose nesting, respectively). These results suggest that food abundance and deterioration of cache sites (e.g., loss of soil cover and partial exposure of caches) were important factors affecting cache loss at our study site. Furthermore, annual variation in the importance of these factors suggests that strategies to prevent cache loss are not fixed in time but vary with existing conditions. Evolution of caching behaviours by arctic foxes may, thus, have been shaped by multiple selective pressures.
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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 | 0.003 | 0.008 |
| 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 | 0.001 | 0.001 |
| 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".