Effects of bovine leukemia virus seropositivity and proviral load on milk, fat, and protein production of dairy cows
Bibliographic record
Abstract
The objective was to evaluate the effects of bovine leukemia virus (BLV) infection, as determined by BLV seropositivity and proviral load, on 305-d milk, fat, and protein production of dairy cows. A cross-sectional study was conducted among 1,712 cows from 9 dairy herds in Alberta, Canada. The BLV status was assessed using an antibody ELISA, whereas BLV proviral load in BLV-seropositive cattle was determined with quantitative PCR. Dairy Herd Improvement 305-d milk, fat, and protein production data were obtained for all enrolled cattle. Differences in these milk end points were assessed in 2 ways: first, by categorizing cows based on BLV serostatus (i.e., BLV positive or negative), and second, by categorizing based on BLV proviral load (i.e., BLV negative, low proviral load [LPL] BLV positive, and high proviral load [HPL] BLV positive). A mixed-effect multivariable linear regression model was used to assess differences in milk parameters. We found that BLV positivity, adjusted for parity and natural log-transformed somatic cell count (SCC), was not associated with reduction in 305-d milk, fat, or protein production. However, significant reductions in 305-d milk, fat, and protein yield occurred in HPL cows, but not in LPL cows, compared with BLV-negative cows, when adjusted for parity number and natural log-transformed SCC. In summary, BLV proviral load may predict effects of BLV infection on milk, fat, and protein production.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".