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Record W2783343590 · doi:10.1111/1749-4877.12302

Effects of sun compass error on spatial search by Clark's nutcrackers

2018· article· en· W2783343590 on OpenAlexaff
Debbie M. Kelly, Ken Cheng, Russell P. Balda, Alan C. Kamil

Bibliographic record

VenueIntegrative Zoology · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Manitoba
FundersNational Institutes of Health
KeywordsCompassLandmarkComputer visionComputer scienceArtificial intelligenceENCODEBiologyGeographyCartography

Abstract

fetched live from OpenAlex

Animals employ compasses during navigation, but little attention has been paid to how accuracy is maintained in the face of compass error, which is inevitable in biological systems. The use of multiple landmarks may minimize the effect of compass error. We allowed Clark's nutcrackers to cache seeds in an outdoor aviary with either one or four landmarks present, and subsequently subjected them to small clock-shifts mimicking the effects of compass error. As predicted, the results showed a significant decrease in search accuracy following the clock-shift when one landmark was present but not when four landmarks were present. These results support that nutcrackers encode information from the sun as well as terrestrial landmarks, and these spatial cues are used in a flexible manner. Overall, our results are important as they support the hypothesis that multiple landmarks may be used during situations where the sun compass has even a small amount of error.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.281
Teacher spread0.260 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations5
Published2018
Admission routes1
Has abstractyes

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