Early-life shedding and environmental presence of Mycobacterium avium ssp. paratuberculosis in Chilean dairy calves
Bibliographic record
Abstract
Neonatal dairy calves are highly susceptible to Mycobacterium avium ssp. paratuberculosis (MAP) infection, but data remain limited on early infection prevalence and transmission drivers. This study aimed to estimate the true prevalence of MAP infection and identify associated risk factors in Chilean dairy calves younger than 60 d of age. Fecal and environmental samples were collected from 579 calves across 39 dairy herds. The MAP detection used a phage-based method (magnetic phage separation-quantitative PCR) that selectively identifies viable bacteria, and a Bayesian model was used to account for biased diagnostics test results. The overall estimated true prevalence of MAP infection was 4.4% (95% posterior probability intervals [PPI]: 0%-19.4%). Environmental contamination with MAP was frequently detected in calf pens (19.4% of pens; 30.7% of herds). Although multiple transmission routes exist, the presence of viable MAP in the calf's immediate pen environment was identified as the primary risk factor associated with infection in these neonatal calves (odds ratio: 3.7; 95% PPI: 1.2-9.0). These findings suggest that MAP infection could occur very early in life in this population, and that contamination within the calf's immediate environment is a key determinant of early-life MAP infection status. This highlights the critical need for implementing rigorous environmental hygiene control measures, specifically within calving and calf-rearing areas, starting immediately after birth, to effectively mitigate early MAP exposure and transmission in 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".