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Record W2075596195 · doi:10.1111/azo.12097

Fibre types in primary ‘flight’ muscles of the African Penguin (<i>Spheniscus demersus)</i>

2014· article· en· W2075596195 on OpenAlexafffund
Kristen Schroeder, Nicole J. Sylvain, Lisa J. Kirkpatrick, Benjamin W. C. Rosser

Bibliographic record

VenueActa Zoologica · 2014
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsUniversity of Saskatchewan
FundersNational Institutes of HealthUniversity of Saskatchewan
KeywordsBiologySeabirdPopulationZoologyCormorantAnatomyEcologyPredation

Abstract

fetched live from OpenAlex

Abstract The African penguin ( Spheniscus demersus ) is an endangered seabird that resides on the temperate southern coast of Africa. Like all penguins it is flightless, instead using its specialized wings for underwater locomotion termed ‘aquatic flight’. While musculature and locomotion of the large Antarctic penguins have been well studied, smaller penguins show different biochemical and behavioural adaptations to their habitats. We used histochemical and immunohistochemical methods to characterize fibre type composition of the African penguin primary flight muscles, the pectoralis and supracoracoideus. We hypothesized the pectoralis would contain predominantly fast oxidative–glycolytic (FOG) fibres, with mainly aerobic subtypes. As the supracoracoideus and pectoralis both power thrust, we further hypothesized these muscles would have a similar fibre type complement. Our results supported these hypotheses, also showing an unexpected slow fibre population in the deep parts of pectoralis and supracoracoideus. The latissimus dorsi was also examined as it may contribute to thrust generation during aquatic flight, and in other avian species typically contains definitive fibre types. Unique among birds studied to date, the African penguin anterior latissimus dorsi was found to consist mainly of fast fibres. This study shows the African penguin has specialized flight musculature distinct from other birds, including large Antarctic penguins.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.878

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)0.0010.000

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 head0.009
GPT teacher head0.181
Teacher spread0.171 · 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

Citations6
Published2014
Admission routes2
Has abstractyes

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