The evaluation of the utility of bulk tank tests for the surveillance of Johne's disease and the effect of storage time and temperature on Johne's milk ELISA results
Bibliographic record
Abstract
The first objective of this study was to evaluate the utility of bulk tank tests to detect the presence of Mycobacterium avium subspecies paratuberculosis (MAP) antibody in dairy herds for the purpose of Johne’s disease surveillance. Individual cow milk samples were collected by CanWest Dairy Herd Improvement customer service representatives in herds across Ontario, Canada. These samples, along with bronopol preserved bulk tank samples were collected from herds participating in the Ontario Johne’s Education and Management Assistance Program (OJEMAP), a producer funded Johne’s control scheme. Overall, there were 309 farms tested, with herd size from 15 to 986 milking cows. The relative sensitivity and specificity of the bulk tank ELISA test when a positive herd was defined as 1 or more positive cows was 54.7% and 90.6%, respectively. The second objective was to determine the effect of milk storage temperature and duration on the Johne’s milk ELISA test result. When herd level factors were considered in a logistic model, average monthly protein (%) and the percent of positive milk contributed to the bulk tank by milk ELISA positive cows were found to be significantly (p<0.05) associated with the probability of a herd testing positive on the bulk tank Hyper ELISA protocol.\nPositive and negative MAP milk samples were stored for varying times and under different temperature conditions. In a mixed linear model, time was found to be significantly (<0.001) associated with the log transformed ELISA optical density. When the results were dichotomized into positive and negative by the cut-off of 0.10 and cross classified, the amount of misclassification was considered biologically negligible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".