Bovine viral diarrhea virus: An updated American College of Veterinary Internal Medicine consensus statement with focus on virus biology, hosts, immunosuppression, and vaccination
Bibliographic record
Abstract
Control of bovine viral diarrhea virus (BVDV) in cattle populations across most of the world has remained elusive in spite of advances in knowledge about this viral pathogen. A central feature of virus perseverance in cattle herds is the unique mechanism of persistent infection. Managing BVDV infection in herds involves controlling persistently infected carrier animals using a multidimensional approach of vaccination, biosecurity, and identification of BVDV reservoirs. A decade has passed since the original American College of Veterinary Internal Medicine consensus statement on BVDV. While much has remained the same with respect to clinical signs of disease, pathogenesis of infection including persistent infection, and diagnosis, scientific articles published since 2010 have led to a greater understanding of difficulties associated with control of BVDV. This consensus statement update on BVDV presents greater focus on topics currently relevant to the biology and control of this viral pathogen of cattle, including changes in virus subpopulations, infection in heterologous hosts, immunosuppression, and vaccination.
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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".