Investigating the dynamics of Johne’s Disease on Ontario dairy farms
Bibliographic record
Abstract
Johne’s disease (JD) is an untreatable disease of ruminants caused by Mycobacterium avium subspecies paratuberculosis. The long latent period and variable manifestation in clinical presentation poses some significant challenges in its detection and control on farms. JD results in economic losses from early culling and production loss due to chronic enteric disease. In January 2010, Ontario began a voluntary control program called the Johne’s Education and Management Assistance program. The program consisted of an on-farm risk assessment survey and whole herd testing by either milk or serum ELISA with subsequent permanent removal of any high titre cow. The purpose of the risk assessment surveys or RAMPs (Risk assessment and management plans) were to identify areas on farm that would result in increased risk of acquiring or transmitting JD. After completing the RAMP, the farm would be given a score out of 300; a higher score correlating with a higher risk of JD. The veterinarian would then make a maximum of three recommendations for improvement of facilities or management for JD control. Based on data collected from over 2,000 dairy farms in Ontario from 2010 to 2013, using individual animal ELISA testing of milk or serum, approximately 26% of farms had at least one test positive animal. Through testing of bulk tank (BT) milk from all Ontario farms in 2013, roughly 50% of farms had a positive bulk tank test for Johne’s. There are numerous barriers to the efficacy of extension programs targeting JD control, and the benefit of risk assessment-based programs for a disease such as Johne’s remains unclear. The objectives of the study are to 1) assess changes in herd-level prevalence of JD 2) to describe associations between RAMP score and herd bulk tank test changes.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".