Bibliographic record
Abstract
The control and prevention of bovine viral diarrhea virus (BVDV) infections has provided substantial challenges. Viral genetic variation, persistent infections, and viral tropism for immune cells have complicated disease control strategies. Vaccination has, however, provided an effective tool to prevent acute systemic infections and increase reproductive efficiency through fetal protection. There has been substantial controversy about the safety and efficacy of BVDV vaccines, especially when comparing killed versus modified-live viral (MLV) vaccines. Furthermore, numerous vaccination protocols have been proposed to protect the fetus and ensure maternal antibody transfer to the calf. These issues have been further complicated by reports of immune suppression during natural infections and following vaccination. While killed BVDV vaccines provide the greatest safety, their limited immunogenicity makes multiple vaccinations necessary. In contrast, MLV BVDV vaccines induce a broader range of immune responses with a longer duration of immunity, but require strategic vaccination to minimize potential risks. Vaccination strategies for breeding females and young calves, in the face of maternal antibody, are discussed. With intranasal vaccination of young calves it is possible to avoid maternal antibody interference and induce immune memory that persists for 6-8 months. Thus, with an integrated vaccination protocol for both breeding cows and calves it is possible to maximize disease protection while minimizing vaccine risks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".