Longitudinal study of the cutaneous microbiota of healthy horses
Bibliographic record
Abstract
BACKGROUND: Next-generation sequencing techniques have revealed that human and animal skin is colonised by a rich and diverse population of bacteria, and that microbial composition varies between different body sites and individuals. Very little is known about the normal microbiota of healthy equine skin. HYPOTHESIS/OBJECTIVES: To describe the taxonomic distributions of cutaneous bacterial microbiota in a population of healthy horses in Ontario, Canada, and to evaluate the effects of body site, individual and time of year on microbial diversity and community composition. ANIMALS: Samples were collected from four body sites (dorsum, ventral abdomen, pastern and groin) from 12 clinically healthy horses from the same farm. Samples were collected from all individuals at four time points (winter, spring, summer, autumn) within a calendar year. METHODS AND MATERIALS: Illumina sequencing of the V4 region of the 16S rRNA gene was performed following DNA extraction. Data were analysed using mothur software. RESULTS: Bacteria from 38 phyla and 1,665 genera were identified. Alpha diversity was higher in the winter and summer than spring and autumn although this was not statistically significant. Community membership and structure clustered more based on season than skin site. CONCLUSIONS AND CLINICAL IMPORTANCE: Healthy equine skin is inhabited by a marked diversity of microbiota. Individuals living in a similar environment share overlapping cutaneous microbial populations. These populations vary significantly over time and between body sites.
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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 | 0.001 | 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".