Wound inflammation post-orchiectomy affects the social dynamic of Nelore bulls
Bibliographic record
Abstract
Abstract Background Confinement of cattle imposes spatial restrictions and predisposes to aversive social encounters that can lead to contusions, wounds, pain, stress, fright, and reduced productivity. Although endogenous testosterone concentrations are linked to agonistic dominance behaviors in males, it is unknown whether decreased blood testosterone concentrations after castration alter social hierarchy rank in Nelore bulls. Therefore, in this study, we investigated the impact of the surgical would inflammation post-orchiectomy on social dynamics in a group of Nelore bulls (Bos indicus). Fourteen Nelore (Bos indicus) bulls were castrated and assessed pre- and post-surgically. Parameters evaluated were agonistic (mounting, headbutting, and fighting) and affiliative (head-play) behavior, plasma testosterone concentrations, average daily weight gain (ADG), and a score for severity of post-surgical infection. Exploratory statistics included social network analysis (SNA), hierarchy rank delta (Δ), and principal component analysis (PCA). Furthermore, statistical inferences included the Wilcoxon test, multiple logistic regression models, and Spearman's correlation. Results The social dynamic of Nelore bulls was modified after castration based on the findings of the SNA and the PCA. The moderate correlation between the postoperative inflammation level with the Δ, and the significant effect of this level in the logistic model post-castration were partially attributed to effects of pain on social relations. Conclusions Our findings suggest the severity of post-surgical inflammation, which has an association with pain intensity, was closely associated with changes in the social hierarchy.
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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 | 0.000 |
| 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) | 0.002 | 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".