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Record W2929570547 · doi:10.3390/ani9040135

An Investigation of Associations Between Management and Feather Damage in Canadian Laying Hens Housed in Furnished Cages

2019· article· en· W2929570547 on OpenAlexafffundabout
Caitlin Decina, Olaf Berke, Nienke van Staaveren, Christine F. Baes, Tina M. Widowski, Alexandra Harlander-Matauschek

Bibliographic record

VenueAnimals · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Guelph
FundersEgg Farmers of Canada
KeywordsFeather peckingFeatherFlockAnimal welfareAnimal-assisted therapyLayingAnimal scienceHUBzeroWelfareBiologyPecking orderVeterinary medicinePet therapyZoologyEcologyMedicineEngineeringEconomics

Abstract

fetched live from OpenAlex

Feather pecking is a continuous welfare challenge in the housing of egg-laying hens. Canada is currently making the transition from conventional cages to alternative housing systems. However, feather damage (FD) among laying hens due to feather pecking remains a welfare concern. An explorative approach was taken to assess bird, housing, and management associations with FD in Canadian laying hens housed in alternative systems. A questionnaire focused on housing and management practices was administered to 122 laying farms across Canada in autumn of 2017 (response rate of 52.5%), yielding information on a subset of 26 flocks housed in furnished cages. Additionally, a three-point feather cover scoring system was developed to estimate the prevalence of FD. Farmers assessed FD by sampling 50 birds per flock. Linear regression modeling was applied to explain FD as a function of 6 variables (out of an available 54). Of the 6 modeled variables, "increased age", "brown feather colour", "midnight feeding", and "no scratch area" were associated with higher levels of FD at farm level (R² = 0.77). The results indicated that FD resulting from feather pecking is a multifactorial problem, and supported existing evidence that FD increases as birds age. These results also suggested that "feather colour", "midnight feeding", and "access to (or lack of) a scratch area or additional substrate" play a role in FD prevalence in furnished cages.

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 score0.135
Threshold uncertainty score0.914

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 head0.024
GPT teacher head0.240
Teacher spread0.216 · 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

Citations16
Published2019
Admission routes3
Has abstractyes

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