Comparison of<i>Staphylococcus aureus</i>Isolates from Bovine and Human Skin, Milking Equipment, and Bovine Milk by Phage Typing, Pulsed-Field Gel Electrophoresis, and Binary Typing
Bibliographic record
Abstract
Staphylococcus aureus isolates (n = 225) from bovine teat skin, human skin, milking equipment, and bovine milk were fingerprinted by pulsed-field gel electrophoresis (PFGE). Strains were compared to assess the role of skin and milking equipment as sources of S. aureus mastitis. PFGE of SmaI-digested genomic DNA identified 24 main types and 17 subtypes among isolates from 43 herds and discriminated between isolates from bovine teat skin and milk. Earlier, phage typing (L. K. Fox, M. Gershmann, D. D. Hancock, and C. T. Hutton, Cornell Vet. 81:183-193, 1991) had failed to discriminate between isolates from skin and milk. Skin isolates from humans belonged to the same pulsotypes as skin isolates from cows. Milking equipment harbored strains from skin as well as strains from milk. We conclude that S. aureus strains from skin and from milk can both be transmitted via the milking machine, but that skin strains are not an important source of intramammary S. aureus infections in dairy cows. A subset of 142 isolates was characterized by binary typing with DNA probes developed for typing of human S. aureus. Typeability and overall concordance with epidemiological data were lower for binary typing than for PFGE while discriminatory powers were similar. Within several PFGE types, binary typing discriminated between main types and subtypes and between isolates from different herds or sources. Thus, binary typing is not suitable as replacement for PFGE but may be useful in combination with PFGE to refine strain differentiation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".