Vaginal and Uterine Microbiota of Healthy Maiden Mares during Estrus
Bibliographic record
Abstract
This descriptive cross-sectional study compared the microbiota of the uterus, vagina, clitoral fossa (CF), and perineal skin in healthy maiden mares during estrus. Twelve synchronized, healthy maiden mares (3–4 years old) from one single recipient mare herd were included. Microbial communities were characterized by amplifying the V3–V4 region of the 16S rRNA gene using the Illumina MiSeq platform. The uterine and vaginal microbiota had significantly lower richness (Chao-1) than the skin (p < 0.05). The uterine and vagina bacterial composition was similar in presence and abundance and could be differentiated from that of the CF and perineal skin. The microbial composition (Jaccard and Bray–Curtis distances) significantly differed across body-site locations (p < 0.05), which explained approximately 14% and 19% of the variation in microbial composition for Jaccard and Bray–Curtis distances, respectively. Firmicutes, Actinobacteria, Proteobacteria, and Bacteroidetes were the dominant taxa in the uterus and vagina, with higher proportions of Proteobacteria in the vaginal samples compared to the uterine samples. Streptococcaceae and Staphylococcaceae were present in high abundance in the uterine and vaginal samples, while Lactobacillaceae were not (<10%). We demonstrate that the uterine and vaginal microbiota of healthy maiden mares during estrus is similar but both distinct from that of the CF and perineal skin.
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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) | 0.001 | 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".