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Parent–environmental interactions shape acoustic signatures in tree swallows: a cross‐fostering experiment

2014· article· en· W2134054850 on OpenAlexafffund
Hendrik Reers, Marty L. Leonard, Andrew G. Horn, Alain Jacot

Bibliographic record

VenueJournal of Avian Biology · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaVolkswagen Foundation
KeywordsBiologyBroodFledgeMaternal effectZoologyEcologyEvolutionary biologyOffspringGeneticsPredation

Abstract

fetched live from OpenAlex

Acoustic signatures are common components of avian vocalizations and are important for the recognition of individuals and groups. The proximate mechanisms by which these signatures develop are poorly understood, however. The development of acoustic signatures in nestling birds is of particular interest, because high rates of extra‐pair paternity or egg dumping can cause nestlings to be unrelated to at least one of the adults that are caring for them. In such cases, nestlings might conceal their genetic origins, by developing acoustic signatures through environmental rather than genetic mechanisms. In a cross‐fostering experiment with tree swallows Tachycineta bicolor , we investigated whether brood signatures of nestlings that were about to fledge were attributable to their genetic/maternal origins or to their rearing environment. We found that the calls of cross‐fostered nestlings did not vary based on their genetic/maternal origin, but did show some variation based on their rearing environment. Control nestlings that were not swapped, however, showed stronger brood signatures than either experimental group, suggesting that acoustic signatures develop through an interaction between rearing environment and genetic/maternal effects.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.794
Threshold uncertainty score0.345

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 head0.031
GPT teacher head0.331
Teacher spread0.300 · 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 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

Citations4
Published2014
Admission routes2
Has abstractyes

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