Control of Mycobacterium avium subspecies paratuberculosis on Western Canadian dairy farms: Prevalence, diagnostics and risk factors
Bibliographic record
Abstract
Mycobacterium avium subspecies paratuberculosis (MAP) causes Johne’s disease (JD), a chronic, nontreatable enteritis of ruminants. The pathogen causes substantial losses to the dairy industry and might be associated with Crohn’s disease in humans. Eradication of MAP through programs that are solely based on ‘test and cull’ is ineffective because current tests lack sufficient accuracy for reliable detection of infected cattle. Consequently, current MAP control programs focus on prevention of new infections through implementation of best management practices. The overall aim of this thesis was to evaluate the Alberta Johne’s Disease Initiative (AJDI), a management-based MAP control program. Research in this thesis focussed on estimating MAP herd-prevalence, evaluating environmental samples as a diagnostic tool, identifying risk factors for MAP infection, and identifying factors that influenced management improvements. A total of 370 farms participated in the AJDI and were visited annually by their herd veterinarians who conducted risk assessments, collected environmental fecal samples, and discussed management changes. Sixty-eight percent of Alberta dairy farms were MAP-infected and environmental samples collected from lactating cow alleyways and manure lagoons were most frequently culture-positive, suggesting that these samples are important to guarantee high environmental sample accuracy. Furthermore, farms with manure-contaminated cattle and pens, poor feed hygiene, or high purchase rates and low purchase precautions were more likely to be MAP-infected; therefore; improvements in these management areas might be most effective in controlling the spread of MAP. Although most farms subsequently improved management, positive test results and agreed management changes increased the rate of management improvements (which were cost effective). It is noteworthy that the current program overlooks hygiene of young cattle, because 2% of heifers shed MAP which indicates that management improvements in this area may reduce MAP transmission.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".