Costs of a Maedi Visna Flock Certification Program and the Changes in Productivity and Economic Output
Bibliographic record
Abstract
Maedi Visna (MV) has been identified as a common viral infection in Ontario sheep. The Maedi Visna Flock Status Pilot Project (MVFSP) sets a protocol for control and eradication of this disease. A static normative model was designed to measure the economic benefit of such a program. Of the 16 producers enrolled on the program in 2002, 15 cooperated and were surveyed. Two benefits were identified from being MV free: 1) higher purebred sheep sale prices and 2) improved ewe productivity. The benefits to purebred sheep breeders warrant eradication within sheep flocks. With only a 10 percent improvement in purebred price, even on only 25 percent of lambs sold for breeding stock, a producer should expect to breakeven on the added costs associated with the MVFCP program just shortly after becoming ‘A’ Status. This outcome was robust for all combinations of flock size, ewe and purebred sheep sale values, and bleeding costs. Commercial sheep producers did not find the same positive outcome. With low prevalence of the disease, few benefits accrued. Only with prevalence levels over 10 percent with low bleeding costs and large flocks would commercial producers show a reasonable payback period of about six years, and then only with the Monitored Program. Payback would never be reached on the Whole-Flock Program for commercial sheep producers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".