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Record W2743759141 · doi:10.1017/s1751731117001896

Development of locomotion over inclined surfaces in laying hens

2017· article· en· W2743759141 on OpenAlexaff
Chantal LeBlanc, Bret W. Tobalske, S. R. Bowley, Alexandra Harlander-Matauschek

Bibliographic record

Venueanimal · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Guelph
FundersBundesamt für Lebensmittelsicherheit und Veterinärwesen
KeywordsClimbingClimbBiologySandpaperWingAnatomyAnimal scienceEcologyStructural engineeringMaterials scienceEngineering

Abstract

fetched live from OpenAlex

The purpose of the present study was to evaluate locomotor strategies during development in domestic chickens (Gallus gallus domesticus); we were motivated, in part, by current efforts to improve the design of housing systems for laying hens which aim to reduce injury and over-exertion. Using four strains of laying hens (Lohmann Brown, Lohmann LSL lite, Dekalb White and Hyline Brown) throughout this longitudinal study, we investigated their locomotor style and climbing capacity in relation to the degree (0 to 70°) of incline, age (2 to 36 weeks) and the surface substrate (sandpaper or wire grid). Chicks and adult fowl performed only walking behavior to climb inclines ⩽40° and performed a combination of wing-assisted incline running (WAIR) or aerial ascent on steeper inclines. Fewer birds used their wings to aid their hind limbs when climbing 50° inclines on wire grid surface compared with sandpaper. The steepness of angle achieved during WAIR and the tendency to fly instead of using WAIR increased with increasing age and experience. White-feathered strains performed more wing-associated locomotor behavior compared with brown-feathered strains. A subset of birds was never able to climb incline angles >40° even when using WAIR. Therefore, we suggest that inclines of up to 40° should be provided for hens in three-dimensional housing systems, which are easily negotiated (without wing use) by chicks and adult fowl.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.236

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)0.0000.000

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 head0.053
GPT teacher head0.285
Teacher spread0.232 · 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

Citations29
Published2017
Admission routes1
Has abstractyes

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