Multi‐Environment Quantification of Parasite and Intermediate Host <scp>DNA</scp> on Pasture for Fine‐Scale Disease Risk Assessment
Bibliographic record
Abstract
ABSTRACT Parasite transmission occurs in complex environments comprising multiple matrices. Trematode parasites of ruminant livestock such as the liver fluke, Fasciola hepatica and the rumen fluke, Calicophoron daubneyi , show affinity with freshwater environments shared with their amphibious snail intermediate host, Galba truncatula . Isolation of environmental DNA (eDNA) from these parasites and their snail hosts in water draining from grazing land provides opportunities for improved molecular diagnostic detection and can help identify infection risks at farm level. The detection and quantification of eDNA from other environmental matrices has received less attention but would improve the understanding of parasite dynamics on pasture. Our study has considerably extended eDNA sampling methods for the detection of parasitic trematodes of ruminant livestock and their snail intermediate host by including for the first time the analysis of soil and herbage environmental samples alongside water collections. A droplet digital PCR (ddPCR) workflow was developed to detect parasite and snail eDNA from soil, herbage, and water collected from livestock farms. For the first time, C . daubneyi eDNA was isolated from agricultural soil alongside water samples and G . truncatula eDNA was detected in water, soil, and herbage samples. No environmental samples were positive for F . hepatica eDNA. Assessing multiple environmental matrices increased the number of positive sites. Future implementation of eDNA detection methods alongside traditional parasite diagnostics can underpin more holistic evaluations of the environmental components of parasite epidemiology and facilitate adaptation to changing disease patterns.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".