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Record W4205671783 · doi:10.1016/j.psj.2022.101728

The effect of infrared beak treatment on the welfare of turkeys reared to 12 weeks of age

2022· article· en· W4205671783 on OpenAlexafffund
S. Struthers, T. Fiss, H.L. Classen, Susantha Gomis, Ryan Dickinson, T.G. Crowe, Eugenia Herwig, K. Schwean-Lardner

Bibliographic record

VenuePoultry Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Saskatchewan
FundersAgriculture and Agri-Food Canada
KeywordsBeakFeather peckingFeatherFlockPecking orderAnimal scienceBiologyQuailBroilerEndocrinologyEcology

Abstract

fetched live from OpenAlex

This study aimed to determine the effects of infrared beak treatment on the behavior and welfare of male and female turkeys reared to 12 wk of age. To do this, poults (236 males and 324 females) were assigned to one of 2 beak treatments: infrared beak treated on day of hatch (IR) or sham untreated control (C). Data collected included heterophil/lymphocyte (H/L) ratio, pecking force, feather cover, behavioral expression, and beak histology. Data were analyzed as a 2 × 2 factorial of beak treatment and gender, in a completely randomized design and analyzed using PROC MIXED (SAS 9.4). H/L ratio (indicative of a stress response) did not differ between treated and control poults during early life, except at 20 d of age when H/L ratio was higher for C poults than IR poults. Pecking force, measured as a method of monitoring pain, was different only at 1 wk of age, when IR poults pecked with more force than C poults. Feather cover was better in IR poults at 12 wk of age. Differences in behavior between treatments were minor over the 12-wk period. Overall, infrared beak treatment of commercial turkeys had minimal negative impacts on behavior and welfare. The results suggest that stress may be reduced in flocks that are beak treated and that the procedure itself does not cause a pain response.

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 score0.904
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.017
GPT teacher head0.234
Teacher spread0.217 · 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

Citations5
Published2022
Admission routes2
Has abstractyes

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