Understanding Salmonella Dublin in British Columbia through bulk tank milk surveillance
Bibliographic record
Abstract
Increased cases of Salmonella enterica ssp. enterica serovar Dublin, a bacterial pathogen that primarily affects dairy cattle, have been noted in British Columbia (BC), Canada, since 2015. The objective of this cross-sectional study was to (1) understand the prevalence and distribution of Salmonella Dublin in BC dairy farms based on bulk tank milk (BTM) serology, (2) to investigate the degree of variability within percent positivity (%PP) in negative and positive farms, and (3) investigate risk factors associated with positivity. All BC dairy herds' BTM was sampled 4 times from September 2021 to April 2023 to determine Salmonella Dublin positivity (at least 1 result ≥35% positivity on BTM serology). Of the 461 herds sampled, 137 (30%) were positive. A multivariable logistic regression model identified 3 risk factors associated with herd level positivity. Specifically, herds with 200 to 500 lactating cattle (odds ratio [OR] = 2.42, 95% CI = 1.14-5.36) and herds with >500 lactating cattle (OR = 21.78, CI = 5.14-152.20) were associated with increased risk of Salmonella Dublin herd level positivity. Farms that had >5 farms within 4 km radius (OR = 1.73, 95% CI = 1.05-2.90) were at greater risk for Salmonella Dublin compared with those with 5 or fewer. Having satisfactory pest control was protective against Salmonella Dublin positivity (OR = 0.28, 95% CI = 0.09-0.80). Percent positivity variability varied greatly among negative, indeterminant (15-34%PP), and positive farms. Negative farms experienced very little variability in %PP (median = 5.79%, interquartile range [IQR] = 3.86%), with variability increasing among the indeterminant farms (median = 16.04%, IQR = 11.94%), and highest among the positive farms (median = 35.84%, IQR = 31.59%). Monitoring variability of BTM %PP could function as an accessible early warning tool for producers and veterinarians to predict if their herds may become positive.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".