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Record W4416661399 · doi:10.1098/rsos.251579

Chicks of cavity-nesting birds do not ‘exercise’ prior to fledging

2025· article· en· W4416661399 on OpenAlexafffund
J. P. Allen, Brett L. Hodinka, Tony D. Williams

Bibliographic record

VenueRoyal Society Open Science · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBird parasitology and diseases
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFledgeWingFacultativeStarlingMechanism (biology)Aerobic exerciseReproduction

Abstract

fetched live from OpenAlex

Fledging represents a key life-history transition involving a rapid increase in workload associated with a rapid transition from sedentary nestling to volant, active fledgling. Here, we tested the idea that chicks might prepare for fledging through increased voluntary activity (‘exercise’) and whether this would impact somatic and physiological development. European starling ( Sturnus vulgaris ) chicks, in cavity nests, increased levels of putative exercise (wing flapping), and more general active behaviours (e.g. perching, standing) in the five days up to fledging. However, facultative mass loss and wing growth between days 15 and 20 were independent of time spent wing flapping, standing or perching and, counterintuitively, we found a weak negative relationship between haematocrit (a measure of aerobic capacity) and time spent wing flapping or standing. Thus, although exercise is commonly associated with an increase in haematocrit in other species, this does not appear to be a mechanism for increasing pre-fledging haematocrit in chicks. Despite widespread anecdotal observations of flight preparation (e.g. wing flapping) in larger seabirds and raptors, our data suggest that exercise, or increased activity in general, does not contribute to improved development just prior to fledging: starling chicks do not ‘exercise’ enough to show somatic or physiological 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)0.001
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 score0.263
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.015
GPT teacher head0.323
Teacher spread0.308 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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