Reducing exposure to pathogens in the horse: a preliminary study into the survival of bacteria on a range of equine bedding types
Bibliographic record
Abstract
AIMS: To compare the rate of growth of four microbial strains that cause disease in the horse, on four commonly used types of bedding. The moisture-holding capacity of each bedding type was also tested. METHODS AND RESULTS: Microbial strains included Streptococcus equi, Streptococcus zooepidemicus, Fusobacterium necrophorum, Dichelobacter nodosus and Dermatophilus congolensis. The bedding types tested were Pinus sylvestris (Scots pine shavings), Pinus nigra (Corsican pine shavings), Picea sitchensis (Sitka spruce shavings), Cannabis sativa (hemp) and chopped wheat straw. A suspension of each microbial strain was spread in triplicate on agar media and incubated in its optimal growth conditions. The viable count (colony-forming unit per ml) was determined for each bacterial strain for the five different bedding types. Pinus sylvestris bedding resulted in significantly less (P = 0·001) bacterial growth of all strains tested. CONCLUSIONS: Factors resulting in the inhibition of bacterial growth include the antibacterial effects reported in the Pinacea family and the physical properties of the bedding substrate. Research is currently focussed on the diagnosis and management of disease. Prevention of disease is also important for matters of biosecurity. Strategies should include the provision of a hygienic environment and the use of specific types of bedding. SIGNIFICANCE AND IMPACT OF THE STUDY: Bedding choice has implications for global equine health and disease prevention as well as potential benefits in other animal species.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".