Current Understanding of Equine Gut Dysbiosis and Microbiota Manipulation Techniques: Comparison with Current Knowledge in Other Species
Bibliographic record
Abstract
Understanding the importance of intestinal microbiota in horses and the factors influencing its composition have been the focus of many studies over the past few years. Factors such as age, diet, antibiotic administration, and geographic location can affect the gut microbiota. The intra- and inter-individual variability of fecal microbiota in horses complicates its interpretation and has hindered the establishment of a clear definition for dysbiosis. Although a definitive causal relationship between gut dysbiosis in horses and diseases has not been clearly identified, recent research suggests that dysbiosis may play a role in the pathogenesis of various conditions, such as colitis and asthma. Prebiotics, probiotics, and fecal microbiota transplantation to modulate the horse's gastrointestinal tract may eventually be considered a valuable tool for preventing or treating diseases, such as antibiotic-induced colitis. This article aims to summarize the current knowledge on the importance of intestinal microbiota in horses and factors influencing its composition, and also to review the published literature on methods for detecting dysbiosis while discussing the efficacy of gut microbiota manipulation in horses.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".