Use of Serology to evaluate the impact of clinical salmonellosis in swine on the herd status and on the contamination of pig carcasses from affected herds.
Bibliographic record
Abstract
Clinical episodes of salmonellosis, often associated with high levels of herd contamination and caused by serotypes other that Choleraesuis, are now observed in Canada. We were thus particularly interested, in this study, to examine the impact of clinical salmonellosis on the serological status of the affected herds and to determine if clinical salmonellosis can be related with an increased level of contamination of carcasses of animals from these herds, after the slaughter process. The Diakit was used as screening test for the detection of Salmonella antibodies in finishing pigs from affected (n=15) and non affected (n=15) herds. The percentage of animal serologically positive to Salmonella in herds that experienced clinical salmonellosis was significantly higher than in herds without clinical sign of salmonellosis (30.0% vs 11.1%, p=0.003). Furthermore, a higher level of contamination of carcasses by Salmonella was found in carcasses from animals from those herds (13.6% vs 2.9%, p=0.048) at slaughterhouse. Herds that were found infected by various serovars from serogroups B (Typhimurium, Derby, Agona, Brandenburg, Heidelberg), C (Ohio, Infantis, Manhattan), E (Senftenberg, Anatum) and N (Urbana) were detected by the ELISA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".