Occurrence of Listeria spp. and Listeria monocytogenes in half-carcasses, meat cuts, equipment, and the environment of bovine slaughterhouses in Brazil
Bibliographic record
Abstract
Human listeriosis is a severe food-borne illness, with fatality rates ranging from 20 to 30 %. In Brazil, despite being an underdiagnosed and underreported disease, the presence of the microorganism in food has been the subject of important studies. However, its occurrence in slaughterhouse environments has received little attention in recent years. Therefore, the aim of this study was to determine the occurrence of Listeria spp. and L. monocytogenes in samples of environments (boning rooms, cold rooms, and slaughter rooms), equipment, half-carcasses, and retail cuts. Samples were collected from 25 slaughterhouse units belonging to five industries under the Federal Inspection Service (SIF) licensed for international trade and located in Brazilian states (São Paulo, Mato Grosso do Sul, Goiás, Mato Grosso, Bahia, Pará, and Minas Gerais), using Polymerase Chain Reaction (PCR). Primers pairs U1/LI1 for Listeria spp., and LM1/LM2 and LL5/LL6 (Invitrogen©) for L. monocytogenes were used for amplification. Listeria spp. was detected in 19.7 % (71/360) of the total samples analyzed, with detection rates of 23 % in deboning rooms, cold rooms, and half-carcasses, 15 % in meat cuts, and 10 % in slaughter rooms. In the total analyzed, L. monocytogenes was detected in 14.7 % (53/360) of the samples, with rates of 20 % in cold rooms, 15.8 % in boning rooms, 13.3 % in half-carcasses and meat cuts, and 10 % in slaughter rooms. The LM1/LM2 primer pair proved more efficient than LL5/LL6. The results of this study highlight the need for urgent measures to control the pathogen in cattle slaughterhouses in Brazil.
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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 | 0.001 |
| 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".