Confirmation of the Johne’s disease negative status of dairy herds as an inclusion criterion to provide calves for a challenge trial
Bibliographic record
Abstract
Determining the herd status of dairy herds in Central Alberta by fecal culture and serum and milk antibody ELISA was performed, to select negative herds to recruit calves for a challenge trial in MAP. In all herds, every animal above 36 months of age, was tested by milk-ELISA, if lactating at the moment of testing, serum ELISA and fecal culture combined with PCR. Calving dates are collected to determine the number of herds tested.\nThe test results are used to declare the status of a herd, though the following considerations should be made when selecting calves. Open or closed farm management influences the significance of the test results. Selecting cows of first and second parity reduces the chance of intrauterine infection. After fecal culture results will be available, definitive selection criteria can be made. Good management around parturition can prevent contamination. \n1094 milk samples were evaluated and 1955 serum samples derived from both lactating and dry cows. Cut-off was defined at 0,60 for serum-ELISA and 0,30 for milk ELISA according to the manual, resulting in 60 (3.1%) positive serum ELISA’s and 33 (3.0%) positive milk ELISA’s. Pearson test, 0.6518, compared serum and milk ELISA results. Kappa value is 0.607. \nSelection criteria of farms was under 5 % seropositive animals, 4 farms were excluded. Fecal culture results, pooled by five, are expected soon. To compare the serum and milk ELISA test results, two tests are used. Wilcoxon signed ranks for ordinal data results in z = -25 and P>0.10. Mc Nemar change test for nominal data results in chi-squared=22, P<0.001. After ranking no difference was found between serum and milk ELISA. \nCollecting calving dates resulted in 126 pregnant cows of first (67) and second (61) parity on 16 farms.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".