Descriptive analysis of the changes in Johne's disease management practices on Ontario dairy farms through repeat risk assessment
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 calves to the pathogen Mycobacterium avium ssp. paratuberculosis (MAP). From 2010 to 2013, Ontario, Canada, instituted a voluntary Johne's control program consisting of whole-herd testing and a Risk Assessment and Management Plan (RAMP). The RAMP consisted of 38 questions that evaluated 5 different management areas to characterize herd risk for MAP introduction and within-herd spread. The RAMP produced a numerical score for each area, with higher scores associated with higher risk. The RAMP focused on animal purchases, calving management, calf management, and heifer and cow cleanliness and management. In the summer of 2019, the RAMP was repeated on 180 farms that had participated in the JD program of 2010 to 2013 and had bulk tank milk ELISA results from 2013 and 2017. This cross-sectional study demonstrated that many producers changed management practices over the 4- to 7-year period. Producers changed their cattle buying practices, with a reduction in purchasing from multiple sources and more herds refraining from buying in animals. However, overall scores were higher in 2019 than in 2013. The 2019 RAMP indicated that fewer farms were utilizing individual calving pens in 2019 than in 2013 (13% vs. 26%), yet more farms had policies in place to deal with sick or suspect JD cows entering the maternity area (92% vs. 74%). Management changes occurred over time, some of which represent increased risk (crowded maternity pens) and others decreased risk (closed herd, protocols in place for JD-positive cows) for MAP introduction and transmission. These results highlight the importance of frequent risk assessments and the documentation of changes to management practices on-farm as a means to assess herd disease risk more accurately.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".