Identification of foot and mouth disease virus carrier and subclinically infected animals and differentiation from vaccinated animals
Bibliographic record
Abstract
Countries that are free of foot and mouth disease (FMD) are reluctant to use vaccine in the event of an outbreak because of the difficulties this can cause in re-establishing freedom from FMD status to the satisfaction of trading partners. The problem does not lie in distinguishing between vaccinated and recovered animals as vaccinated animals can be tagged or otherwise marked to show that they have been vaccinated; the difficulty is in identifying vaccinated animals that have had contact with live virus and become carriers. The traditional probang test is not sufficiently sensitive and is labour- and laboratory-intensive, but alternative serological tests such as those for antibodies to non-structural proteins (NSPs), or specific immunoglobulin A (IgA) are also not 100% sensitive. However, these newer tests do provide increased security by reducing the likelihood of trading carrier animals and can be used to help define the limits of an outbreak; the use of vaccine to help control an outbreak of FMD in a previously free country still has significant consequences on trade in FMD susceptible animals and their products.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".