Providing a rest stop during transportation affects the respiratory bacterial microbiota of beef cattle
Bibliographic record
Abstract
Background Bovine respiratory disease (BRD) is a significant health concern in beef cattle production, leading to substantial economic losses. In North America, beef cattle are frequently transported over long distances for placement into feedlots. The respiratory microbiota of cattle, including pathogens, can change after feedlot entry. However, there is limited information on how bacteria are impacted when cattle are offloaded for a rest stop during transportation. Results This study investigated the effects of a rest stop during transportation on the nasopharyngeal (NP) microbiota of beef cattle. Two separate trials (N = 80 calves per trial) were conducted with treatment groups assigned to rest stop durations of 12 h (Study 1) and 8 h (Study 2), being compared to control animals without a rest stop. In Study 1, cattle were acclimated to a feedlot prior to transportation while in Study 2, cattle were unacclimated. Following transportation and a rest interval, calves were placed into a feedlot and sampled by NP swab periodically for 28 days. Across treatments and time, the most abundant genera included Mycoplasma, Histophilus, Mannheimia, Pasteurella, Moraxella, and Acinetobacter. In both studies, microbial diversity and structure were not affected by providing a rest stop. However, NP swabs from more sampling time points had elevated levels of the BRD-associated genera Mannheimia, Histophilus, and Mycoplasma when the microbiota of calves provided rest were compared to animals given no rest. Conclusion Based solely on the increased abundance of BRD-associated bacteria, providing a rest stop during transportation may be a risk factor for BRD. However, it was not possible to associate rest stop-induced changes in microbiota with disease outcome due to a low incidence of BRD. Further evaluation using large-scale studies will help define the impact of a rest stop during transportation, on BRD pathogens and incidence in feedlots.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".