An observational analysis of Canada Jay (<i>Perisoreus canadensis</i>) foraging and caching ecology in Denali National Park and Preserve, Alaska, USA
Bibliographic record
Abstract
Arctic and subarctic wildlife are among the most vulnerable species to climate change. Canada Jays (Perisoreus canadensis (Linnaeus, 1776)) are generalist residents of northern boreal forests and scatter-hoard food to insulate against food scarcity during winter. Unlike most scatter-hoarders, however, Canada Jays primarily cache perishable food, rendering their caches more susceptible to climate change induced degradation and loss. Here we use a mostly noninvasive approach to document Canada Jay foraging ecology among a population in interior Alaska, USA, including the types of food acquired, foraging and caching rates, and cache longevity and loss. We also tested for associations between foraging and caching rates with reproductive metrics to assess possible relationships among food and productivity. We found that Canada Jays have a varied diet that changed seasonally, and responded to a record-setting warm spring by directing foraging efforts away from cache recovery and towards the emergence of fresh food. We did not find evidence for relationships between foraging and caching rate with reproductive output, possibly owing to small sample sizes. We found that caches were recovered quickly (<4 weeks) and frequently lost to conspecific and heterospecific competitors. Our study suggests that Canada Jays may be better poised to respond to changes in cache integrity and food availability than has been previously recognized.
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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) | 0.001 | 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".