Temporal patterns of bovine leukemia virus infection in dairy herds in Atlantic Canada.
Bibliographic record
Abstract
Objective: The primary objective was to determine the youngest age group where bovine leukemia virus (BLV)-infected dairy animals were identified. The secondary objective was to investigate associations between age-specific management practices and BLV infection status of different age groups of dairy calves and heifers. Procedure: For enrolled herds, BLV status was determined using blood samples from pre-weaned calves, weaned calves, and breeding-age heifers; and bulk tank milk from the adult herd. A questionnaire investigating age-specific management factors was administered for each herd. Ordinal logistic regression was performed to identify management factors associated with the youngest age range in which BLV was identified. Results: Fifty-three dairy herds from the 4 provinces in Atlantic Canada were enrolled. Bovine leukemia virus was most commonly earliest identified in pre-weaned heifers (18 herds, 32.1%) and the adult herd (18 herds, 32.1%). Ordinal logistic regression revealed that BLV was first identified in older age groups more often than in younger age groups when herds regrouped weaned heifers at least once, when fly control was used for breeding-age heifers, when herds practiced foot trimming on breeding-age heifers, and when bred heifers were brought in. Conclusion: Producers can use results to identify the youngest age group(s) in which BLV is identified and to tailor management strategies to prevent new infections.
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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 | 0.000 | 0.001 |
| 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".