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Record W3212535165 · doi:10.1098/rsos.211579

Nest decoration: birds exploit a fear of feathers to guard their nest from usurpation

2021· article· en· W3212535165 on OpenAlexafffund
Tore Slagsvold, Karen L. Wiebe

Bibliographic record

VenueRoyal Society Open Science · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFeatherNest (protein structural motif)FicedulaCyanistesBiologyEcologyFlight featherPredationNest boxZoologyContext (archaeology)Parus

Abstract

fetched live from OpenAlex

Many species of birds incorporate feathers into their nest as structural support and to insulate the eggs or offspring. Here, we investigated the novel idea that birds reduce the risk of nest usurpation by decorating it with feathers to trigger a fear response in their rivals. We let prospecting birds choose between a dyad of nest-boxes in the wild, both containing some nest materials, but where one had a few white feathers and the other had none. All three species of cavity-nesting birds studied, the pied flycatcherFicedula hypoleuca, the blue titCyanistes caeruleus, and the tree swallowTachycineta bicolor, hesitated to enter boxes with white feathers. A similar avoidance of white feathers was found when the alternative nest-box of a dyad held black feathers. However, the birds readily collected white feathers that we placed in front of their nest-box, showing the fear of such feathers was context-dependent. We suggest that naive prospecting birds may perceive feathers in nests as the result of a predation event, and that owners decorate nests with bright feathers that can be seen from the opening to deter others from entering.

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)0.000
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.040
GPT teacher head0.278
Teacher spread0.238 · 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

Citations10
Published2021
Admission routes2
Has abstractyes

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