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Record W3042695887 · doi:10.3390/ani10071223

Perch Positioning Affects both Laying Hen Locomotion and Forces Experienced at the Keel

2020· article· en· W3042695887 on OpenAlexaff
Christina Rufener, Ana K. Rentsch, Ariane Stratmann, Michael J. Toscano

Bibliographic record

VenueAnimals · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Guelph
FundersBundesamt für Lebensmittelsicherheit und Veterinärwesen
KeywordsKeelPerchSlammingAnatomyBiologyMathematicsFisheryStructural engineeringEngineeringMarine engineeringFish <Actinopterygii>

Abstract

fetched live from OpenAlex

The aim of this study was to assess the effect of perch positioning on laying hens' locomotion and the resulting energy experienced at the keel. Twenty Nick Chick and 20 Brown Nick hens were trained to transition from a platform to a perch in several configurations. Three variables of perch positioning were tested in a 2 × 2 × 2 factorial design: direction (upward vs. downward), angle (flat vs. steep), and distance (50 cm vs. 100 cm). All hens were tested for five jumps of each treatment combination at 27-28 weeks of age. As predicted, we found steep angles and long distances to result in higher peak forces and impulse during take-off, flight, and landing; longer latency to jump; a higher likelihood to perform balancing movements; and a longer latency to peck at the provided food reward. The effect of perch positioning on locomotion and force at the keel during downwards jumps and flight was more pronounced in Brown Nick hens than in Nick Chick hens. Although we cannot state how the observed forces at the keel relate to the risk for keel bone fractures, our results indicated that optimizing perch positioning can reduce accumulated forced at the keel and consequent risk for fracture due to unsuccessful transitions.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.974
Threshold uncertainty score0.516

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 head0.032
GPT teacher head0.235
Teacher spread0.204 · 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

Citations14
Published2020
Admission routes1
Has abstractyes

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