Influence of handling in corrals on the temperament of different breeds of beef cattle raised in Brazil
Bibliographic record
Abstract
The objective of the present study was to verify the effect of handling in corrals on the stress of beef cattle of Nelore, Caracu, and Guzerá breeds through the analysis of the relationship between behavioral traits, growth, and blood parameters. A total of 778 records of entry scores, chute scores, exit scores, flight speed, cortisol, glucose, lactate, live weight, and body condition scores were collected on steers and heifers of the three breeds, born between September and December. The animals came from the Advanced Beef Cattle Research Center of the Animal Science Institute. The variance analyses were performed using the GENMOD procedure. The PROC CORR estimated Spearman correlation coefficients for all traits studied, and the principal components analysis was performed according to the procedure PRINCOMP. Heifers had higher reactivity than steers, with higher behavioral scores and blood parameters. Overall, Caracu individuals demonstrated lower reactivity in comparison to Nelore and Guzerá. However, they presented high stress when they were restrained. The principal component analysis highlighted that behavioral traits and blood parameters were contrasted with growth traits; thus, this analysis can help selectors (livestock farmers) understand the importance of selecting less reactive animals and more easily choose the animals they want to select for breeding. Our results indicate that handling in corrals could be considered a stressful event for bovines. For this reason, measuring blood parameters and behavioral traits can compose necessary decision-making tools regarding changes and adequacies while handling events to reduce stress and consequently improve the herd's reactivity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".