Evaluation of oral fluid as an aggregate sample for early detection of African swine fever virus using four independent pen‐based experimental studies
Bibliographic record
Abstract
The sustained spread of African swine fever (ASF) virus throughout much of the world has made ASF a global animal health priority, with an increased emphasis on enhancing preparedness to prevent, detect and respond to a potential outbreak of ASF virus (ASFV). In the event of ASFV entry to the North American swine population, enhanced surveillance and diagnostic testing strategies will be critical to facilitate progressive response and eradication of the disease. Compared to individual animal sampling, pen-based oral fluid collection for active surveillance is a non-invasive alternative that is less resource and time-intensive. To evaluate the feasibility of using rope-based oral fluid for early detection of ASFV, four independent animal experiments were conducted in weaned pigs housed in numbers that mimic the industry settings, utilising either highly virulent ASFV Georgia 2007/1 strain or moderately virulent ASFV Malta'78 strain. Pen-based oral fluid and individual oropharyngeal swabs were collected daily and blood samples from each animal were collected every other day. All samples were subsequently tested for ASFV by real-time PCR. ASFV genome was detected in individual blood samples as early as one day post-infection and detected in oral fluids at low-to-moderate levels as early as 3-5 days post-infection in all four independent experiments. These results suggest that pen-based oral fluid samples may be used to supplement the use of traditional samples for rapid detection of ASFV during ASF surveillance.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".