Development and implementation of a risk assessment and management program for enzootic bovine leukosis in Atlantic Canada
Bibliographic record
Abstract
Over the past 30 yr, the prevalence of bovine leukemia virus (BLV) infection has increased in North America, including Atlantic Canada, at both the herd and individual cow levels. This has occurred despite increased awareness of the disease and its deleterious effects and despite implementation of management practices aimed at reducing disease transmission. Our objectives were to identify risk factors associated with the within-herd prevalence of BLV-infected cows by using a risk assessment and management program workbook, as well as to determine the current level of BLV prevalence in the Atlantic Canada region. We hypothesized that previously established risk factors, including management practices associated with calf rearing and fly control, would affect within-herd BLV prevalence. Bulk tank milk (BTM) samples were collected in January and April of 2016 and again during the same months in 2017 and 2018 from all dairy farms shipping milk in the region. Samples were tested with ELISA for levels of anti-BLV antibodies to estimate within-herd prevalence. Regional BLV prevalence at the herd level was 88.39% of dairy herds infected in 2016 and 89.30% in 2018. All dairy farms shipping milk and who had BTM samples collected in 2017 (n = 605) were eligible to participate in the risk assessment and management program questionnaire (RAMP), which was developed and distributed to all bovine veterinarians in Atlantic Canada. One hundred and six RAMP were returned, with representation from all 4 provinces. The RAMP results were combined with the mean BTM ELISA results, and univariable logistic regression followed by multivariable logistic regression was performed to investigate the association between RAMP risk factors and the estimated within-herd BLV prevalence. Factors in the multivariable model significantly associated with the odds of a herd being classified as >25% estimated within-herd prevalence included history of diagnosis of clinical BLV and calves receiving colostrum from cows with unknown BLV status. Differences in within-herd prevalence were not associated with hypodermic needle and injection practices, rectal sleeve practices, or using bulls for natural breeding, based on these 106 dairy farms.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".