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Record W2466974987 · doi:10.3382/japr/pfw037

Does age of hot-blade trimming impact the performance and welfare of 2 strains of White Leghorn hens?

2016· article· en· W2466974987 on OpenAlexaff
K. Schwean-Lardner, C.B. Annett-Christianson, J. Rajendram, H.L. Classen

Bibliographic record

VenueThe Journal of Applied Poultry Research · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsAlberta Ministry of Agriculture and ForestryUniversity of Saskatchewan
Fundersnot available
KeywordsBeakTrimmingBiologyAnimal scienceFeather peckingFactorial experimentPecking orderZoologyEcologyMathematics

Abstract

fetched live from OpenAlex

The impact of mild hot-blade beak trimming on welfare and performance in 2 strains of White Leghorn pullets was examined. During the pullet phase, 960 pullets were designated to one of 4 trimming treatments: control (untrimmed, C), trimmed at a commercial hatchery (T0d), or trimmed on farm at 10 d (T10d) or 35 d (T35d) of age. During the hen phase, 720 of the original 960 hens were housed in conventional cages at 17 wk of age (6 replications per strain × treatment group) and data were analyzed as a 2 × 4 (performance and beak length data), 2 × 2 (behavior data, blocked by observer) factorial arrangement or a Chi-Squared analysis (beak healing). Total hen-day production tended to be lower for C birds, but hen-housed production did not differ. Feed intake was not affected by trimming treatments, but feed efficiency was poorer for C birds. Treatment did not statistically affect mortality. Cannibalism, although not significantly different among treatments, occurred in C birds only. The C pullets displayed more head/vent pecking, but no differences were noted in adult birds. There was no behavioral or histological evidence of chronic pain or neuroma formation, and healing occurred quicker when trimming occurred at zero or 10 d of age. To conclude, trimming mildly at zero, 10, or 35 d of age caused no long-term effect on welfare or performance, but trimming at younger ages resulted in faster healing.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.946
Threshold uncertainty score0.257

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 studies0.0000.001
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 head0.047
GPT teacher head0.311
Teacher spread0.263 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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