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Record W2049025211 · doi:10.1242/jeb.064394

MIGRATING GEESE: NATURALLY ATHLETIC COUCH POTATOES?

2012· article· en· W2049025211 on OpenAlexaff
Jessica U. Meir

Bibliographic record

VenueJournal of Experimental Biology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBarnacleGooseBiologyJuvenileEcologyZoologyPectoral muscleFisheryGeographyCrustaceanAnatomy

Abstract

fetched live from OpenAlex

Migrating birds accomplish the extraordinary feat of trans-oceanic and even trans-hemispheric treks, a true marvel of the animal kingdom. Prior to these excursions, many bird species display dramatic changes in morphology, withering less crucial organs and increasing essential flight components like heart and flight muscle. Although bulking up muscle in humans necessitates an increase in their use or exercise, recent evidence in birds suggests an additional endogenous capacity for muscle gain, independent of behaviour or the environment. Barnacle geese (Branta leucopsis) are known to increase production of key metabolic proteins prior to migration. Wild, flighted birds of this species have significantly higher levels of these proteins than do flightless captive or juvenile geese. This suggests that for this species, flight training may be crucial to migratory readiness. With this knowledge, Steve Portugal of the University of Birmingham (now at the UK's Royal Veterinary College) and his international team set out to further investigate these trends, predicting that long-distance migrants like the barnacle goose might increase flight activity prior to migration to achieve a heftier physique and stimulate essential protein production.The team caught wild barnacle geese near Norway's Ny-Ålesund research station, implanted custom-made heart rate loggers into the birds' abdomens, and recaptured the birds the following year. The data loggers monitored the heart rate of the birds continuously throughout the year, including both the spring and autumn migrations. As it is known that heart rate increases dramatically during flight, these records allowed the researchers to construct flight activity patterns. Using this strategy, the team calculated the time in flight for each day for each goose in addition to comparing heart rate patterns during distinct annual phases (pre-migration, migration, breeding and wing-moult). Portugal's crew discovered that, contrary to their original hypothesis, there was no difference in the time spent flying per day in the pre-migratory phase compared with any other period. As a buildup of flight muscle prior to migration has previously been documented in this species, this implies that increased flight activity is not required to achieve their burly physique.Other bird species show strong relationships between pre-migratory mass gain and increased flight muscle. It is possible, then, that the corresponding increases in wing loading due to the heftier build documented in pre-migration barnacle geese triggers an increase in flight muscle. With this loading effect, the usual amount of time spent flying (just 22 min day−1 for these geese) may be adequate to achieve buildup of flight muscle prior to migration, and potentially induce essential protein production. It is also possible that any endogenous capacity for muscle building in waterfowl may support this pre-migratory couch potato approach. Nonetheless, this study reveals that a flight-training regime is not required to prime the barnacle goose for its impressive long-distance migration. As flight is the most costly form of vertebrate locomotion, 10–20 times more energetically expensive than the resting state, this may also reflect a strategy to conserve vital energy stores for the remarkable journey that lies ahead.

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 score0.011
Threshold uncertainty score0.022

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 communication0.0010.001
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 head0.014
GPT teacher head0.291
Teacher spread0.277 · 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
Published2012
Admission routes1
Has abstractyes

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