ENZOOTIC BOVINE LEUKOSIS AND FOOD SAFETY: RISKS AND CONTROL APPROACHES
Bibliographic record
Abstract
Bovine enzootic leukosis (EBL), caused by the Bovine leukemia virus (BLV), is a retroviral disease of major veterinary and economic significance worldwide. Although the primary impact is on animal health, the presence of viral genetic material in milk and lymphoid tissues has raised concerns regarding food safety and possible zoonotic potential. Pasteurization considerably reduces viral infectivity, but complete inactivation remains debated, while ultra-high temperature (UHT) treatment eliminates viral RNA. BLV has not been detected in bovine muscle tissue, yet lymph nodes, spleen, and liver may act as reservoirs. The European Union applies strict eradication programs and prohibits the marketing of raw milk from BLV-positive cattle, while the United States and Canada permit products from infected animals provided thermal processing is ensured. This paper provides a systematic review of scientific evidence, international legislation, and risk management strategies. The findings emphasize the importance of harmonized global standards, strict hygiene, and continued surveillance to ensure consumer confidence and protect food safety.
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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".