Clark’s Nutcrackers (Nucifraga columbiana) Flexibly Adapt Caching Behavior to a Cooperative Context
Bibliographic record
Abstract
Corvids recognize when their caches are at risk of being stolen by others and have developed strategies to protect these caches from pilferage. For instance, Clark's nutcrackers will suppress the number of caches they make if being observed by a potential thief. However, cache protection has most often been studied using competitive contexts, so it is unclear whether corvids can adjust their caching in beneficial ways to accommodate non-competitive situations. Therefore, we examined whether Clark's nutcrackers, a non-social corvid, would flexibly adapt their caching behaviors to a cooperative context. To do so, birds were given a caching task during which caches made by one individual were reciprocally exchanged for the caches of a partner bird over repeated trials. In this scenario, if caching behaviors can be flexibly deployed, then the birds should recognize the cooperative nature of the task and maintain or increase caching levels over time. However, if cache protection strategies are applied independent of social context and simply in response to cache theft, then cache suppression should occur. In the current experiment, we found that the birds maintained caching throughout the experiment. We report that males increased caching in response to a manipulation in which caches were artificially added, suggesting the birds could adapt to the cooperative nature of the task. Additionally, we show that caching decisions were not solely due to motivational factors, instead showing an additional influence attributed to the behavior of the partner bird.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".