Nose-Flap Devices Used for Two-Stage Weaning Produce Wounds in the Nostrils of Beef Calves: Case Report
Bibliographic record
Abstract
This case report aims to describe the occurrence of negative impacts of wearing nose-flap devices on beef calves subjected to the two-stage weaning method. Forty-one calves, twenty-one pure Nellore and twenty F1 Angus-Nellore, were weaned on average at 236 days of age. Commercial nose-flap devices were fitted in the nostrils of the calves (d0) to prevent suckling and removed five days later (d5). Individual body weights were assessed at d0 and d5, and average daily gain (ADG) was calculated. At d5, during nose-flap device removal, it was noted that 26.8% of the calves lost the nose-flap device; however, all of them had wounds in their nostrils (no injuries in the nostrils had been observed on d0). To assess the severity of these injuries, an impairment score was assigned to each calf, ranging from 1 = no lesions to 5 = injured with purulent discharge. A logistic regression model was fitted to evaluate the effect of sex and genetic group on nose-flap retention (kept or lost). The retention rate did not differ (p > 0.05) between sex and genetic groups. All calves showed at least open wounds of the nasal septum (score 2), including those that lost the nose-flaps before d5. Almost half of the calves showed weight loss during this period. We conclude that there is a considerable risk of the two-stage weaning method compromising the physical integrity of the nostrils of beef calves through the use of these devices, and due to this, it should not be referred to as a low-stress weaning practice for beef calves.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".