MétaCan
Menu
Back to cohort
Record W4389146515 · doi:10.1098/rsos.230817

Reduction of wing area affects estimated stress in the primary flight muscles of chickens

2023· article· en· W4389146515 on OpenAlexafffund
Grace A. T. Hong, Bret W. Tobalske, Nienke van Staaveren, Emily M. Leishman, Tina M. Widowski, Donald R. Powers, Alexandra Harlander-Matauschek

Bibliographic record

VenueRoyal Society Open Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Guelph
FundersAgriculture and Agri-Food CanadaOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsWingFeatherBird flightFlight featherBiologyAnatomyLeg muscleWingspanAnimal scienceZoologyMedicineEcologyPhysicsPhysical medicine and rehabilitationMoulting

Abstract

fetched live from OpenAlex

In flying birds, the pectoralis (PECT) and supracoracoideus (SUPRA) generate most of the power required for flight, while the wing feathers create the aerodynamic forces. However, in domestic laying hens, little is known about the architectural properties of these muscles and the forces the wings produce. As housing space increases for commercial laying hens, understanding these properties is important for assuring safe locomotion. We tested the effects of wing area loss on mass, physiological cross-sectional area (PCSA), and estimated muscle stress (EMS) of the PECT and SUPRA in white-feathered laying hens. Treatments included Unclipped ( N = 18), Half-Clipped with primaries removed ( N = 18) and Fully-Clipped with the primaries and secondaries removed ( N = 18). The mass and PCSA of the PECT and SUPRA did not vary significantly with treatment. Thus, laying hen muscle anatomy may be relatively resistant to changes in external wing morphology. We observed significant differences in EMS among treatments, as Unclipped birds exhibited the greatest EMS. This suggests that intact wings provide the greatest stimulus of external force for the primary flight muscles.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.980
Threshold uncertainty score0.170

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.043
GPT teacher head0.283
Teacher spread0.240 · 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 designBench or experimental
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

Citations4
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueRoyal Society Open ScienceSame topicAnimal Nutrition and PhysiologyFrench-language works237,207