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Record W4402541120 · doi:10.1093/jas/skae234.008

271 Does social buffering amongst familiar weaned beef calves mitigate castration stressors?

2024· article· en· W4402541120 on OpenAlexaff
R. L. Parsons, Caleb Brezina, Grant A. Dewell, Reneé D. Dewell, Anna K. Johnson, Derek B. Haley, Suzanne T. Millman

Bibliographic record

VenueJournal of Animal Science · 2024
Typearticle
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAnimal scienceStressorCastrationBiologyEndocrinologyHormone

Abstract

fetched live from OpenAlex

Abstract Co-mingling of beef feeder calves from multiple sources results in biological and behavioral stressors. This study is part of a project exploring social relationships between individual beef cattle and potential benefits of social buffering during stress events. Here, the specific objective was to explore whether feeder calves form preferential relationships which buffer them from surgical castration pain. Within the experiment, two treatment groups were established: Familiar calves were defined as calves who shared a source farm with two other calves in the pen, and Unfamiliar calves were defined as calves who did not share a source with any other pen-mates. It was hypothesized that Familiar calves would exhibit more lying, fewer pain-related behaviors, and fewer agonistic interactions following surgical castration than Unfamiliar pen-mates. Weaned beef bull calves [n = 102, body weight (BW) range ± SD = 112.95 to 370.10 ± 67.8 kg] from 23 source farms were randomly assigned to home pens, each comprised of three Familiar calves and three Unfamiliar calves. Cameras placed above the pens recorded video from which postural (standing and lying), social (agonistic and bunk displacements) and pain-related behaviors (wound checking and hind leg stamping) were quantified. Cattle were handled through a hydraulic squeeze chute once weekly over four consecutive weeks for data collection and standard processing procedures. On d 14 relative to arrival, calves were surgically castrated with multimodal pain mitigation. During all processing events, calves were marked with livestock paint for behavioral observations. Behavior was observed continuously for 15 min at five different time points (0 h, 1 h, 4 h, 24 h, 48 h) relative to when the last calf returned to the home pen after processing, and researchers were blind to treatment allocation. These time periods corresponded to expected periods when pain was absent and present based on duration of local anesthesia and systemic pain relief. Calf was the experimental unit. For the purpose of this abstract, data from the 48 h periods following d 7 and 14 were analyzed using the PROC GLIMMIX procedure of SAS; the model included treatment (Familiar, Unfamiliar) by week interaction. A P-value of ≤ 0.05 was considered significantly different. Regardless of treatment, preliminary analyses identified decreased lying (P < 0.02) and increased wound checking (P < 0.01) 48h following castration (d 14) when compared with the previous week (d 7). There were no observed differences for hindleg stamping (P = 0.2), agonistic behaviors (P > 0.06) or bunk displacement (P = 0.09). Based on preliminary analyses, we did not observe evidence of social buffering effects on castration stress during the immediate post-surgical period.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.048
GPT teacher head0.356
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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