Interpretation of repeated testing for Mycobacterium avium subsp paratuberculosis on Ontario dairy herds
Bibliographic record
Abstract
The Ontario Johne's Education and Management Assistance Program (OJEMAP) is a dairy industry-funded Johne's disease (JD) control program launched in January 2010. The program offers dairy producers a one-time opportunity to test their adult cow herd for antibodies against Mycobacterium avium subspecies paratuberculosis (MAP) by use of a milk or blood ELISA test. As herds completed their milking herd test, a common question from herd owners and veterinarians was "When should I test next .... how often should I test my herd?" A review of aggressive JD control programs around the world suggests that there are no standard recommendations regarding testing frequency. The Danish program, in existence for 7 years, is based on quarterly testing of the entire milking herd using a milk ELISA. Dr. Soren Nielsen, who developed the Danish Program and serves as its director, argues that given the relatively poor sensitivity of all JD tests, quarterly testing allows each cow to have at least 3 test results per year, the results of which can then be used to classify cows as at high-, moderate-, or low-risk for transmitting MAP to herd mates. Implementation of this strategy has led to a decrease in JD test-positive prevalence from 10% to 6% of cows in herds participating in the program. Unfortunately, this aggressive herd test program carries a high cost and the benefits of quarterly testing over a single annual test are difficult to quantify. The objective of this project was to evaluate and gain experience in the interpretation of repeated testing for MAP with currently available tests in Ontario dairy herds.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".