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Record W2070312285 · doi:10.1242/jeb.064170

THE NOT-SO-PLAIN DUET OF THE PLAIN-TAILED WREN

2012· article· en· W2070312285 on OpenAlexaff
Viviana Cadena

Bibliographic record

VenueJournal of Experimental Biology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAnimal Vocal Communication and Behavior
Canadian institutionsBrock University
Fundersnot available
KeywordsSingingDuration (music)ThicketBiologyCommunicationEcologyZoologyAcousticsPsychologyArtLiterature

Abstract

fetched live from OpenAlex

Among the beautiful, colourful birds inhabiting the Andean mountains of Ecuador, plain-tailed wrens seem rather underwhelming; little drab-looking birds hopping around bamboo thickets looking for the insects that will be their next meal. However, despite their dull appearance, plain-tailed wrens’ songs are anything but plain. Like a stereo recording, you can hear the song coming from two places at once, as it is a perfectly coordinated duet where the male and female rapidly alternate syllables of the tune. The syllables don’t overlap, as each of the singers leaves gaps where their partner interjects with remarkable precision.A team of scientists from the US and Ecuador, led by Eric Fortune at Johns Hopkins University, investigated how plain-tailed wrens are able to coordinate their amazing duet. From over 150 h of audio recordings, the scientists were able to extract and analyse over 1000 songs from plain-tailed wrens. They discovered that while most of the time pairs of wrens sang together, sometimes both males and females sang by themselves, each singing their part of the song, leaving gaps where their counterpart would normally interject. Because the duration of these gaps was larger and more variable during solo singing than during duets, the authors conclude that wrens do not just follow a fixed pattern when producing their song, but instead rely on auditory cues from their partner to determine the length of the gaps between syllables. Interestingly, the songs of solo males were more variable and infrequent than those of females, who were frequently recorded singing by themselves. Moreover, sometimes males made mistakes during a duet, failing to sing their part of the song. On these occasions, the female would continue singing her part, leaving larger gaps between her syllables until the male joined in again. These observations suggest that female plain-tailed wrens may be the leading duetting partner.Fortune and his team also examined how the brain of the wrens encoded the song. They captured six birds and recorded the responses of individual neurons in the part of the brain responsible for learning and production of songs (the high vocal centre). The scientists played back the birds’ own duets as well as individual syllables from the male and female singers. The reaction of both the males and females was strongest to the duet, and was larger than the reactions to either the male or female solos or even the sum of the two responses together. However, both males and females exhibited a more pronounced response to the female syllables alone than to the male song. These results demonstrate that the complete song is encoded in both male and female wren brains and, again, suggests that females take the leading role.The findings from this study might reveal the mechanisms of cooperative behaviour that occurs among other animals. Each partner needs to know the part they play, but they also need to be able to receive cues from their partner in order to know when and how to play their own part. Moreover, they both need to be more tuned in to the leader’s cues for the operation to succeed. It takes two to tango, after all.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.026
GPT teacher head0.326
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 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".

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Citations0
Published2012
Admission routes1
Has abstractyes

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