Short communication: Herd-level prevalence of Mycobacterium avium ssp. paratuberculosis is not associated with participation in a voluntary Alberta Johne’s disease control program
Bibliographic record
Abstract
Johne's disease (JD) control programs for dairy farms have the general objective of reducing both cow- and herd-level prevalence of Mycobacterium avium ssp. paratuberculosis (MAP). An important aspect of many programs is herd testing for MAP to determine the infection status of participating farms. However, it is uncertain whether MAP herd-level prevalence on farms voluntarily participating in a JD control program is different from that on nonparticipating farms. Therefore, the aim was to compare MAP infection status of participants and nonparticipants in the Alberta Johne's Disease Initiative (AJDI), a voluntary JD control program initiated in 2010 in Alberta, Canada. Between September 2012 and August 2013, environmental fecal samples were collected from 93 randomly selected farms not enrolled in the AJDI. Additionally, 81 farms that initially enrolled in the AJDI during the same time interval were also sampled. Samples were collected from 6 defined locations on each farm and cultured for MAP. Results were confirmed using conventional IS900 PCR and F 0285 quantitative PCR. Overall, 51% of participating and 51% of nonparticipating farms were identified as being MAP-infected. Furthermore, based on multivariable logistic regression, the number of MAP-positive samples was not associated with AJDI participation (taking herd size into account as a potentially modifying or confounding variable). In conclusion, there was no indication that voluntary participation in the AJDI was associated with herd-level MAP prevalence.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".