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Record W2321568775 · doi:10.1242/jeb.077735

COOL SONGS ARE SEXY FOR LINCOLN'S SPARROW CHICKS

2013· article· en· W2321568775 on OpenAlexaboutno aff
Viviana Cadena

Bibliographic record

VenueJournal of Experimental Biology · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
Fundersnot available
KeywordsChorusSparrowSingingBasal metabolic rateArtHistoryZoologyBiologyLiteratureAcousticsPhysics

Abstract

fetched live from OpenAlex

Most people will agree that a nicely performed song by a guy hoping to impress a girl will go a long way. This is particularly true in the world of birds, where singing is one of the main ways males try to seduce females. Interestingly, many songbirds focus their serenading efforts at dawn, which is usually the coldest time of day. But why would they want to do this? Singing at dawn represents a thermal challenge: metabolic rate increases in the cold and therefore the task of singing becomes more energetically costly. But perhaps that is exactly why they do it, to impress females with their cold tolerance and dazzling performance. If so, then female birds would find songs sung in the cold sexier than songs sung in warm weather. To test this hypothesis, Michaël Beaulieu and Keith W. Sockman from the University of North Carolina, USA, studied the songs of Lincoln's sparrows at different temperatures.Lincoln's sparrows are common throughout Canada and parts of the USA. The average temperature during their song chorus is 7.8°C, well below the temperature (23°C) beneath which metabolic rates rise above basal levels. To determine whether female Lincoln's sparrows are more attracted to songs performed at cold temperatures than songs performed in warm conditions, Beaulieu and Sockman divided females into two groups, and played them songs performed by two males. First, they exposed one group of females to one male's songs at 16°C and played the second male's songs at 1°C, recording how long the females spent next to the loudspeaker. However, they had to be sure that the females weren't simply hanging out by the speaker because they preferred that male's recital, so they reversed the temperatures when they played the songs to the second group of females, playing the first set at 1°C and the second set at 16°C. The researchers also tested whether the females would remember if a particular song had been sung in the cold and if they preferred it over a song that they had heard at warmer temperatures.Having analysed the females' preferences, the team found that the female sparrows spent 40% more time close to the speaker when they were serenaded in the cold than when they were listening to love songs in mild conditions. Furthermore, even at 16°C, females preferred the song they had initially heard in the cold, so songs performed in the cold are clearly sexier than songs that are warbled when it's warm.For songbirds, singing in the cold seems to be a way of advertising the quality of a male in the form of cold tolerance. The preferred choice of females for males singing at low temperatures might represent their choice of a male that may be energetically fitter and may be able to endure energetic challenges better than soft males who sing in the warm. The fact that females remembered which songs were performed in the cold might allow females to remain in the roost or perform other activities or while listening and then reciprocate the male's advances later in the day – when timing and temperature may be more convenient. Unfortunately, females' preferences for males with the stamina to serenade on chilly mornings may be severely affected by climate change as morning temperatures rise, although one thing is clear: cool singing is hot for birds.

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 score0.004
Threshold uncertainty score0.015

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 studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.030
GPT teacher head0.281
Teacher spread0.251 · 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
Published2013
Admission routes1
Has abstractyes

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