Update on Senecavirus Infection in Pigs
Bibliographic record
Abstract
family. The virus has been silently circulating in pig herds of the USA since 1988. However, cases of senecavirus-associated vesicular disease were reported in Canada in 2007 and in the USA in 2012. Since late 2014 and early 2015, an increasing number of senecavirus outbreaks have been reported in pigs in different producing categories, with this virus being detected in Brazil, China, and Thailand. Considering the novel available data on senecavirus infection and disease, 2015 may be a divisor in the epidemiology of the virus. Among the aspects that reinforce this hypothesis are the geographical distribution of the virus, the affected pig-producing categories, clinical signs associated with the infection, and disease severity. This review presents the current knowledge regarding the senecavirus infection and disease, especially in the last two years. Senecavirus epidemiology, pathogenic potential, host immunological response, diagnosis, and prophylaxis and control measures are addressed. Perspectives are focused on the need for complete evolutionary, epidemiological and pathogenic data and the capability for an immediate diagnosis of senecavirus infection. The health risks inherent in the swine industry cannot be neglected.
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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".