Factors affecting management changes on farms participating in a Johne’s disease control program
Bibliographic record
Abstract
Modern Johne's disease programs aim to control Mycobacterium avium ssp. paratuberculosis (MAP) infection through implementation of management practices that reduce the probability of MAP introduction and within-herd transmission on dairy farms. Success of these programs depends on whether weaknesses in management are corrected through implementation of management improvements. The objectives of this study were, therefore, to (1) assess whether scores in risk-assessment (RA) questions predicted suggestions for management changes for the upcoming year; and (2) determine factors as assessed in an RA that motivated producers to make management improvements and assess whether management improvements were influenced by previously received test results. The RA determining on-farm management related to MAP introduction and transmission were conducted annually by herd veterinarians on 370 dairy farms participating in the Alberta Johne's Disease Initiative. A maximum of 3 management changes that the farmer and the veterinarian agreed upon were recorded in a management plan. The MAP infection status of the herds was assessed through culture of 6 environmental samples. Whereas a management change was proposed for only 4% of questions with scores 1 or 2 (low risk), a change was proposed for 19% of questions with scores >2 [high risk; odds ratio (OR)=11.4]. Improvement in RA question scores was more likely between the first and second annual RA than between the second and third RA (OR=1.6). Farms with >3 culture-positive environmental samples collected in the previous year were more likely to improve their management than environmental sample culture-negative farms (OR=1.3). In conclusion, proposed management changes were oriented toward previously identified weaknesses in management practices, suggesting that the RA was properly used to design targeted management suggestions. Furthermore, improvements in management were not randomly distributed among farms participating in the control program. Instead, knowledge of MAP infection status of a herd, suggestions for management improvements, and duration of participation all influenced implementation of management improvements.
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 | 0.002 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".