An investigation of the microbiological quality of smoked fish in England and detection of <i>Listeria monocytogenes</i> strains associated with listeriosis
Bibliographic record
Abstract
AIMS: A survey to determine the presence of Listeria and indicator bacteria in smoked fish was performed from 2022 to 2023. METHODS AND RESULTS: Listeria monocytogenes was detected in 3.6% of 783 samples. Listeria monocytogenes was detected more frequently in cold smoked (4.9%) compared to hot smoked (2.0%) samples. The majority (63%) of samples had a water activity that would not prevent the growth of L. monocytogenes. Elevated levels of Enterobacteriaceae (>10 000 CFU g-1) were detected in 4.7% of samples. Listeria monocytogenes was detected more frequently in samples with elevated levels of Enterobacteriaceae. Isolates of L. monocytogenes were compared using whole genome sequencing to historic isolates. Isolates from three samples were the same strain as that associated with a clonal complex (CC) 217 outbreak under investigation and detected despite an earlier product recall. Twelve isolates were CC121 strains and 10 of these belonged to six single nucleotide polymorphism single-linkage clusters with one strain linked to a human case from 2021. One CC415 strain was also linked to a human case. The remaining isolates belonged to CC8, CC9, CC14, CC37, CC31, CC398, CC87, and CC475 with no links to human cases. Investigation of historical isolates revealed that 11 strains were associated with specific manufacturers for up to 10 years. The presence of quaternary ammonium compound efflux genes showed an association with CC. CONCLUSIONS: Diverse L. monocytogenes detected in smoked fish with persistent strains associated with some manufacturing premises. Correlation between presence of L. monocytogenes and high levels of Enterobacteriaceae was detected.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".