Seroprevalence of <i>Mycobacterium avium</i> spp. <i>paratuberculosis</i> in cow-calf herds located in the prairie provinces of Canada.
Bibliographic record
Abstract
Objective: (MAP) in cow-calf herds located in the prairie provinces of Alberta, Saskatchewan, and Manitoba using a serum ELISA test. Animals: SN) designed to monitor factors related to the health and productivity of cow-calf herds. Overall, 1791 cows from 92 herds were included in the study. Procedure: Blood samples were collected from 20 cows per herd in a systematic random fashion by private veterinarians in the fall of 2014. A serum ELISA (IDEXX, Westbrook, Maine, USA) test was used for the detection of MAP antibodies in the blood samples. Results: The cow level seroprevalence across all 3 provinces was 1.5%. Alberta had the lowest cow seroprevalence (1.3%) followed by Saskatchewan (1.7%), and Manitoba (2.1%). Herd level data showed that 24% of herds had at least 1 positive animal and 5% had at least 2 positive animals. Seroprevalence estimates varied between geographical regions within each province and with herd size. Conclusions: The apparent prevalence of MAP in prairie cow-calf herds remains low and similar to past estimates for the region. However, controlling the spread of Johne's disease in the western Canadian cow-calf herd should be considered an important discussion point in the beef industry. Clinical relevance: Ongoing surveillance of Johne's disease in western Canadian beef herds is necessary for mitigating disease spread before it becomes a disease of major concern within the industry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".