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Record W4408042440 · doi:10.1007/s10071-025-01939-0

Pinyon Jays (Gymnorhinus cyanocephalus) and Clark’s nutcrackers (Nucifraga columbiana) can discriminate between pilfering and non-pilfering conspecifics, but not between heterospecifics

2025· article· en· W4408042440 on OpenAlexafffund
Alizée Vernouillet, Nanxi Huang, Debbie M. Kelly

Bibliographic record

VenueAnimal Cognition · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyEcologyBiology

Abstract

fetched live from OpenAlex

When foraging, individuals often need to assess potential risk from competitors. Within many food-caching (food-storing) species, individuals can modify their caching behavior depending on whether other individuals are present during the caching event. During caching, individuals may interact with not only conspecifics but also heterospecifics. However, the extent to which individual cachers can discriminate between conspecifics and heterospecifics that present a pilfering threat or not, has received little attention. During this study, we examined this issue with food-storing birds, highly social pinyon jays and less social Clark's nutcrackers. Cachers were given a choice to store their seeds in one of two visually distinct trays. Subsequently, one of the trays was given to an individual (either a conspecific or a heterospecific) who pilfered the caches, whereas the other tray was given to an individual (either a conspecific or a heterospecific) who did not pilfer the caches. When the two trays were returned to the cachers, they recached the seeds from the tray given to the pilfering observer individual more so than the tray given to the non-pilfering observer, but only when the pilferer was a conspecific. Our results suggest that the pinyon jays and nutcrackers could distinguish between conspecifics based on their pilfering behavior, but not between heterospecifics. Together, our results reconsider the ability of corvids to discriminate between individuals based on their pilfering risk and the importance of doing so while caching.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.676
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.281
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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