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Record W4416276795 · doi:10.1163/1568539x-bja10337

Diminishing avoidance over time of nest cavities containing feathers by passerine birds

2025· article· W4416276795 on OpenAlexafffund
Tore Slagsvold, Karen L. Wiebe

Bibliographic record

VenueBehaviour · 2025
Typearticle
Language
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFeatherPasserineNest (protein structural motif)Nest boxPredationPredatorAvoidance behaviour

Abstract

fetched live from OpenAlex

Abstract Secondary cavity nesting birds often investigate unfamiliar, dark holes while prospecting for a nest site. Such cavities may be dangerous to enter if there is a concealed predator or other occupant inside or if a predator returns to the cavity. Previous experiments on passerine birds showed that fear of entering unexplored cavities by prospecting birds was triggered by a few light-coloured feathers in the cavity which may act as cues of predation. Decorating the nest with feathers may thus reduce the risk of nest usurpation and egg dumping. We tested whether this fear of feathers also deters breeding by letting prospecting pairs of blue tits Cyanistes caeruleus and pied flycatchers Ficedula hypoleuca choose between a dyad of nest boxes which were similar in size and interior colour, but where only one nest box contained three white hen feathers and the other nest box served as a control. As many as 40% ( ) of the pairs chose the box with feathers, suggesting that most of the fear of feathers diminishes soon after the cavity is first entered. Features of the nest box (size and internal colour) did not seem to affect the strength of avoidance of the feathers. The degree of avoidance was similar in the two species, and most pairs of both species removed feathers from the nest box if they eventually used it for nesting.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.007
GPT teacher head0.244
Teacher spread0.237 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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