Attributing salmonellosis cases to foodborne, animal contact and waterborne routes using the microbial subtyping approach and exposure weights
Bibliographic record
Abstract
Salmonella is a major cause of enteric disease in Canada. Cases of salmonellosis were attributed to retail meats, food animal manure contact, and surface water sources using a microbial subtyping approach coupled with adjustments for exposure. Results indicated that 64.7% of cases were attributed to chicken breast meat, followed by frozen raw breaded chicken products (12.9%), ground chicken (9.1%), water (3.0%), pork chops and sausage (1.3%), ground beef and veal (0.7%), turkey parts (0.5%), and molluscs (0.0%). The salmonellosis incidence rate in the FoodNet Canada sentinel sites fell by one third with a parallel drop of one third in the percent of cases attributed to chicken breast meat between 2015 and 2019. Decreases in the contribution of many of the top serovars to the percentage of cases attributed to chicken breast meat indicates some emerging success with broiler breeder chicken vaccination programs. In addition, preliminary prevalence results for frozen raw chicken products in late 2019 suggests the Canadian Food Inspection Agency intervention in 2019 requiring any Salmonella on these products to be below a detectable amount may be having an impact, though more data post intervention is needed to be more conclusive.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".