A Holistic Investigation of Johne's Disease Control on Ontario Dairy Farms through Quantitative and Qualitative Methods
Bibliographic record
Abstract
This thesis presents a holistic investigation of Johne’s disease (JD) management and control. Follow up risk assessment and management plans (RAMPs) were conducted on 180 Ontario dairy farms. From this, the changes in management practices that occurred on study farms since the end of the Ontario Johne’s Education and Management Assistance Program (OJEMAP), were described. This information from the follow-up RAMPs, along with the results of province-wide bulk tank (BT) milk enzyme-linked immunosorbent assays (ELISA) were used in developing logistic regression models that described the odds of successful JD control using the RAMP results as predictors. Repeat ELISA test results from 1,197 cows from a previously completed longitudinal study were used in random forest models to generate a predictive algorithm that classified milk testing results as positive or negative. Finally using 20 in-depth interviews with Ontario dairy producers, we explored the motivations and barriers producers experience with regards JD control and farm biosecurity. \n\tThe provincial BT ELISA results demonstrated that the prevalence of JD positive BT’s increased from 46.8 to 71.4% in a 4 year period. Along with this, many changes to the management practices had occurred on Ontario dairy farms since the end of the OJEMAP in 2013. Notably, many producers had decreased their risk of JD through cattle additions. The resulting logistic regression models suggested that management of the calving area and calving practices were significantly associated with JD control. Specifically, farms were more likely to have BT positive results when they calved multiple cows in the maternity pen at the same time and less likely to have BT positive results when they calved their cows outside of dedicated maternity areas. The random forest algorithms were able to predict which test results would be positive for JD using the milk testing data and history of JD testing. The model was able to correctly classify over 80% of the tests within its top 25% of predictions. Unsurprisingly, interest in JD control had diminished since the end of the OJEMAP. Without evidence of clinical signs, and no financial support for diagnostic testing, few producers believed it was an issue they needed to pursue.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".