The nasopharyngeal microbiota of feedlot cattle
Bibliographic record
Abstract
The bovine nasopharyngeal tract plays an important role in animal health and welfare by acting as a site for the carriage of pathogens causing bovine respiratory disease, a condition which results in significant morbidity and mortality in feedlot cattle. We characterized the bacterial nasopharyngeal microbiota in cattle at feedlot entry (day 0) and day 60 using 454 pyrosequencing. We also identified the most frequently isolated aerobic bacteria from nasopharyngeal swabs after plating onto three types of media. The cattle nasopharyngeal microbiota was composed primarily of Proteobacteria (68.9%) and Firmicutes (19.2%). At the genus-level, there was more inter-individual variability and a total of 55 genera were identified. The genera Pseudomonas (23.7%), Shewanella (23.5%), Acinetobacter (17.5%), and Carnobacterium (12.2%) were most prevalent at entry, while after 60 days in the feedlot, Staphylococcus (20.8%), Mycoplasma (14.9%), Mannheimia (10.4%), and Moraxella (9.4%) were dominant. The nasopharyngeal microbiota also became more homogenous after 60 days in the feedlot and differed in structure at day 0 and 60. Using culture-based methods, the most frequently isolated bacteria from nasopharyngeal swabs were Bacillus, Staphylococcus, Moraxella, Pasteurella, and Mannheimia. These results provide insight into the nasopharyngeal microbiota of cattle and demonstrate that specific changes take place during feedlot production.
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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".