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Record W4399584470 · doi:10.1007/s00265-024-03485-0

Birds do not use social learning of landmarks to locate favorable nest sites

2024· article· en· W4399584470 on OpenAlexaff
Tore Slagsvold, Karen L. Wiebe

Bibliographic record

VenueBehavioral Ecology and Sociobiology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsUniversity of Saskatchewan
FundersUniversitetet i Oslo
KeywordsNest (protein structural motif)CyanistesFicedulaParusAnimal ecologyBiologyAvian clutch sizeNest boxEcologyZoologyPredationReproduction

Abstract

fetched live from OpenAlex

Abstract Experiments since the classic studies of Niko Tinbergen have provided evidence that animals use visual landmarks to navigate. We tested whether birds use visual landmarks to relocate their nest sites by presenting two species of cavity nesting birds with a dyad of nest boxes with different white markings around the entrance, a circle or a triangle. When the two boxes were erected in close proximity on the same tree, pied flycatchers Ficedula hypoleuca and blue tits Cyanistes caeruleus confused the entrance of their nest both when the boxes had different external markings and when they were unmarked. Most females added nest material to both boxes of a dyad and one third of the flycatchers laid eggs in both boxes although a female can only incubate the eggs in one nest at a time. Thus, the birds did not use external markings around cavity entrances for orientation. We also tried to replicate a previous study purporting to show that migratory birds use social learning of the external appearance of nests from other species. However, pied flycatchers did not choose boxes with the same painted markings as those applied to nests of resident great tits Parus major which were judged to be high quality “demonstrators” from their large clutch sizes. We argue that conclusions from previous studies on social learning based on external markings as landmarks on nest cavities in birds need to be reconsidered.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score0.558

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 head0.060
GPT teacher head0.340
Teacher spread0.280 · 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.

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

Citations3
Published2024
Admission routes1
Has abstractyes

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