Impact of high proviral load on milk production, reproduction and subclinical diseases in dairy cows infected with bovine leukemia virus
Bibliographic record
Abstract
Introduction: Bovine Leukemia Virus (BLV) prevalence remains high in dairy cattle in North America. Quantifying the proviral load (PVL) in BLV-positive cows can be used to control this disease in herds where BLV is prevalent by focusing culling of high PVL animals to reduce the risk of transmission. The impact of high BLV PVL on dairy cows' performance is not well established. The objective of this study was to assess the effect of high PVL status on milk production, occurrence of subclinical ketosis or mastitis, or fertility in BLV-infected cows. Methods: Twenty-five herds from the three Maritime provinces in Atlantic Canada were enrolled in this study. BLV infected cows were first identified by individual milk or serum testing. A validated quantitative qPCR was used to quantify the PVL in cows with positive BLV antibody results. Parity, 305-day milk production, annual geometric average somatic cell count, fat-to-protein ratio in milk on the first test post-calving, days in milk at first service, and calving-to-conception interval were collected from DairyComp305 software. Two-level mixed multivariable regression models were used to assess the relationship between BLV PVL and milk production, subclinical mastitis and ketosis and reproduction performance. Results: High PVL was strongly associated with reduced milk production (387 kg and 431 kg) and reproduction performance (calving-to-conception interval lengthened by 50 days and 49 days), and higher odds of subclinical mastitis (Odds ratio = 2.38 and 2.48), when compared to BLVpositive cows with a low PVL and BLV-negative cows, respectively. Conclusion: These results support implementing a control program to prioritize culling high PVL cows.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".