<i>Salmonella</i> Serovars and Antimicrobial Resistance Profiles in Beef Cattle, Slaughterhouse Personnel and Slaughterhouse Environment in Ethiopia
Bibliographic record
Abstract
The present study was undertaken to determine the occurrence, distribution and antimicrobial resistance profiles of Salmonella serovars in slaughter beef cattle, slaughterhouse environment and personnel engaged in flaying and evisceration during slaughtering process. A total of 800 samples (each sample type, n = 100) consisting of swabs from hides, slaughterhouse personnel hands at flaying and evisceration, rumen and caecal contents, mesenteric lymph nodes, carcasses and holding pens were collected. Of the total 100 beef cattle examined, 14% were Salmonella positive in caecal content and/or mesenteric lymph nodes. Of the various samples analysed, Salmonella was detected in 31% of hides, 19% of rumen contents, 8% of mesenteric lymph nodes, 6% of caecal contents, 2% of carcass swabs, 9% of palm swabs taken from the hands of personnel in the slaughterhouse during flaying (7%) and evisceration (2%), and in 12% of holding pen swabs. The Salmonella isolates (n = 87) belonged to eight different serovars of which S. Anatum (n = 54) and S. Newport (19) were the major serovars and both serovars were detected in all sample sources except in carcass swabs. Eighteen of the 87 (20.7%) Salmonella serovars consisting of Newport (n = 14), Anatum (n = 3) and Eastbourne (n = 1) were resistant to one or more antimicrobials. Among the antimicrobial resistant Salmonella serovars, S. Newport was multidrug resistant (15.6%) and exhibited resistance to streptomycin, sulphisoxazole and tetracycline.
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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 | 0.000 |
| 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 | 0.000 |
| 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".