Bovine leukemia virus infection in cattle of China: Association with reduced milk production and increased somatic cell score
Bibliographic record
Abstract
The main objective of this study was to investigate the individual cow effect of bovine leukemia virus (BLV) infection on milk production and somatic cell score (SCS). The fluorescence resonance energy transfer (FRET) quantitative PCR established in this study and a commercial ELISA kit revealed that 49.1% of dairy cattle (964/1,963) from 6 provinces of China and 1.6% of beef cattle (22/1,390) from 15 provinces were BLV positive. In a detailed study of 105 cows, BLV was found most commonly in buffy coat samples that also had highest copy numbers (10(4.75±1.56) per mL); all cows negative for BLV in buffy coat samples were also negative in vaginal swab, milk, and fecal samples. Copy numbers of BLV were 10(2.90±0.42)/gram of feces, 10(0.83±0.62)/mL of milk, and 10(2.18±0.81) per vaginal swab. The BLV-positive cows had significantly lower milk production in the early (26.8 vs. 30.9kg) and middle stages of lactation (22.2 vs. 26.1kg) in animals with ≥4 parities than the BLV-negative cows; they also had significantly higher SCS in early and middle lactation stages (early=5.2 vs. 4.3; middle=4.9 vs. 3.9) in animals with ≥4 parities. Milk production and SCS did not significantly differ between the BLV-infected and -uninfected cows when they were in the late lactation stage or in animals with ≤3 parities. Taken together, our results indicate that BLV infections are widespread in the dairy farms of China. Vaginal secretions and feces may be involved in BLV transmission. A BLV infection may result in reduced milk yield and increased SCS in a parity and lactation stage-restricted manner.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".