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Record W7047885082

The impact of flight feather loss and exercise involving inclines on the main flight muscles, keel bone health and aerial descent in domestic laying hens

2023· dissertation· en· W7047885082 on OpenAlexfundno aff

Bibliographic record

VenueThe Atrium (University of Guelph) · 2023
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsnot available
FundersMinistry of Agriculture, Food and Rural AffairsAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Agriculture, Food and Rural Affairs
KeywordsKeelDescent (aeronautics)Bird flightFeatherPectoral muscleFlight featherWingLeg muscle
DOInot available

Abstract

fetched live from OpenAlex

Domestic chicken are bipedal birds that are capable of terrestrial and aerial locomotion. As flapping flight is energetically costly, chickens may use a combination of their hindlimbs and wings to locomote; however, little is known about the relationship between the keel bone, the flight muscles that anchor to it and the chickens’ ability to navigate their environment. Therefore, this thesis investigated the effects of feather loss (Chapter 2) and exercise using inclines on flight muscle architecture, keel bone fractures (Chapter 3) and flight kinematics (Chapter 4). Altogether, the results of this thesis address gaps in knowledge on flight muscle architecture in laying hens. Although exercise did not influence flight muscle properties and subsequently had little effect on keel fracture prevalence, it did benefit the hindlimbs, producing faster take-off velocities and allowed white-feathered birds to have slower accelerations, contributing to more efficient transitions to the air and safer landings.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.262
Teacher spread0.244 · 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 designQualitative
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

Citations1
Published2023
Admission routes1
Has abstractyes

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