Economic evaluation of participation in a voluntary Johne's disease prevention and control program from a farmer's perspective–The Alberta Johne's Disease Initiative
Bibliographic record
Abstract
The Alberta Johne's Disease Initiative (AJDI) is a Johne's disease (JD) control program with the goal of reducing the spread of Mycobacterium avium ssp. paratuberculosis (MAP) through implementation of best management practices. The objective was to estimate the economic benefit of participation in the AJDI. A decision tree was constructed in which disease prevalence, test characteristics, and probabilities for implementation of best management practices suggested by herd veterinarians were implemented. Analysis was performed using a Markov analysis, and input data were assigned using estimates from the AJDI and published data. A cost-effectiveness analysis was performed and the net benefit of participation (from the perspective of a dairy farmer) in the AJDI compared with no participation was calculated. A series of 1-way sensitivity analyses were used to control for uncertainty. Farms participating in the AJDI were estimated to have a net benefit of Can$74 per cow over the course of 10 yr. If project costs were covered by the participating farm, the net benefit was Can$27. In addition to the effects on MAP infection, a reduction in calf diarrhea was modeled for farms that improved their calf management through the use of pasteurizers. In that case, the additional costs outweighed additional revenues compared with the baseline analysis, resulting in a reduced net benefit of Can$19. Participation would not be cost effective if cows in early stages of MAP infection did not have decreased production and if prevalence of MAP infection did not increase on farms with poor management. A limitation of the study, despite high uncertainty in some input parameters, was the lack of knowledge regarding changes in prevalence on farms with various management strategies. In conclusion, participation in the AJDI was cost effective for the average Alberta dairy farm.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".