Characterization of bacterial populations recovered from the teat canals of lactating dairy and beef cattle by 16S rRNA gene sequence analysis
Bibliographic record
Abstract
Bovine mastitis is of major concern to the dairy industry worldwide. The bovine teat canal is the primary route through which pathogens enter the mammary gland. The microflora of the teat canals of dairy and beef cattle was investigated by analysis of 16S rRNA gene sequences. The goal was to examine the global difference between dairy cattle, which are sanitized on a regular basis, and beef cattle, which receive little sanitary management. A diverse population of 16S rRNA gene sequences was recovered from both the dairy and the beef herd samples, with diversity higher in the beef sample. Analysis revealed the presence of 90 operational taxonomic units (OTUs) among 156 sequences, with 45 OTUs in the dairy sample and 53 OTUs in the beef sample. Only eight OTUs were common to both samples. Members of the classes Clostridia and Bacilli dominated both samples, followed by Actinobacteria and Proteobacteria. The dairy sample contained a cluster (20/80 clones) of Staphylococcaceae members, seven of which were identifiable as coagulase-negative Staphylococcus species. The beef sample was dominated by members of the genus Clostridia, many of which have not been previously cultured. The results suggest that the microorganisms present in the bovine teat canal are more diverse than previously described.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".