Canada jays (<i>Perisoreus canadensis</i>) identify and exploit coniferous cache locations using visual cues
Bibliographic record
Abstract
Abstract Food caching is a foraging strategy used by many vertebrates, involving the storage and subsequent recovery of food items for later consumption, when other food sources are scarce. Once cached, stored food, particularly highly perishable items, can degrade over time. Evidence suggests that for birds, some conifers may aid in cache preservation through protective properties in resin. However, due to the challenges involved with following birds to their caching locations, cache‐site preferences are not easily studied in the wild. We investigated eight captive Canada jays’ (Perisoreus canadensis) ability to both identify and exploit conifer tree species. Further, we examined potential cues that birds may use to identify and select these potentially beneficial sites. We found strong evidence to suggest that birds can quickly identify conifer tree species and subsequently exploit those cache locations preferentially. Furthermore, our evidence suggests that although birds do not appear to use olfactory cues when making caching decisions, they potentially to attend to structural cues. We suggest that visual information is essential to both the identification of conifer trees and to cache‐site selection decisions. These findings indicate that jays make rapid, fine scale assessments of their environments, discriminating amongst trees of different species and use this information to select cache‐sites.
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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) | 0.002 | 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".