Risk factors associated with presence of Salmonella
Bibliographic record
Abstract
Abstract Carriers of Salmonella in finishing pigs are believed to be a main source of carcass and pork contamination at the slaughterhouse and during meat processing. To better control the pres-ence of Salmonella at the farm and slaughter, it is important to adequately identify on-farm risk factors associated with presence of these bacteria in animals. Thus a study was performed to identify the risk factors associated with the presence of Salmonella in live animals and on car-casses from 312 herds in Canada. A questionnaire was designed to gather information on several factors present on the farm and was completed by the producers. Results from the multivariate logistic regression analyses of all the factors indicated a significant effect of the type of feed grind (p = 0.0001), pen size (p=0.006), truck load capacity (p=0.008), clinical signs of salmonellosis (p=0.03) and a marginal effect of number of sources of finishing pigs (p = 0.098). Carcasses from highly contaminated herds were more likely of being Salmonella positive. Limiting the presence of Salmonella in the meat should therefore begin by reducing the number of herds highly contaminated by Salmonella. Introduction Many HACCP-based good production practices to be used on farm have been devel-oped in order to manage biological hazards, such as Salmonella and chemical hazards. The pur-
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".