Removing bovine leukemia virus–infected animals with high proviral load leads to lower within-herd prevalence and new case reduction
Bibliographic record
Abstract
Bovine leukosis is prevalent in the North American dairy industry, and its effect on animal health and production is widely documented. However, not all bovine leukemia virus (BLV)-infected animals transmit the virus equally. Animals with high proviral loads (HPL) of BLV are associated with higher transmission risks, and therefore, their removal may reduce transmission and eventually within-herd prevalence. We aimed to evaluate the impact of selectively removing HPL cows on the within-herd BLV prevalence and incidence rate of BLV infection in 10 dairy herds. Annual blood or milk samples (or both) were collected from adult cows over 3 yr. Positivity with BLV were determined by ELISA tests, and proviral loads in blood of BLV-positive animals were estimated with BLV SS1 quantitative PCR assays. Herd managers were encouraged to consider the proviral load when making culling decisions and implement BLV control practices. Cows with high proviral load had the highest relative risk of removal, indicating the farmers prioritized HPL cows for culling. The within-herd BLV prevalence decreased significantly in 4 herds, whereas BLV incidence rate decreased in 9 herds. Over the 3 yr, the proviral load demonstrated a relatively stable level, suggesting a single proviral load test in an adult cow may suffice to make culling decisions.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".