Management practices associated with Johne's bulk tank milk ELISA positivity
Bibliographic record
Abstract
Johne's disease (JD) control is often based on the culling of positive animals and the adoption of management practices that minimize exposure of young stock to the pathogen (Mycobacterium avium ssp. paratuberculosis). Throughout 2010 to 2013, the province of Ontario, Canada, instituted a voluntary Johne's control program consisting of whole-herd testing and risk assessment. The JD risk assessment evaluated 5 management areas to characterize herd JD risk. Using a modified milk ELISA technique with an optical density cut-off of 0.089, province-wide bulk tank milk (BTM) testing was used to assess the prevalence of JD high-risk herds at the end of the control program and again 4 yr after its completion. Approximately 71% of Ontario bulk tanks were classified as positive in 2017 compared with roughly 46% in 2013. In 2019, the same JD risk assessment used in the original program was readministered on 180 Ontario dairy farms. Using this cross-sectional approach, logistic regression models were built using data from the original program risk assessment and follow-up risk assessment as well as the BTM ELISA results to determine management factors associated with the control of JD. We demonstrated that management of the maternity area is an important factor in the control of Johne's disease. Although it is believed that the highest risk group for JD infection is calves under 6 mo, the cleanliness scores of older heifers and their exposure to mature cow manure was significantly associated with JD control; farms with highly contaminated weaned and bred heifers and those that had exposure to mature cow manure were more likely to be unsuccessful in their JD control efforts. Careful management of young calves appears to be important for JD control, and this management should continue even after calves have left the maternity area.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".