Comparison of the Prevalences and Diversities of Listeria Species and Listeria monocytogenes in an Urban and a Rural Agricultural Watershed
Bibliographic record
Abstract
Foods and related processing environments are commonly contaminated with the pathogenic Listeria monocytogenes. To investigate potential environmental reservoirs of Listeria spp. and L. monocytogenes, surface water and point source pollution samples from an urban and a rural municipal water supply watershed in Nova Scotia, Canada, were examined over 18 months. Presumptive Listeria spp. were cultured from 72 and 35% of rural and urban water samples, respectively, with 24% of the positive samples containing two or three different Listeria spp. The L. innocua (56%) and L. welshimeri (43%) groups were predominant in the rural and urban watersheds, respectively. Analysis by the TaqMan assay showed a significantly (P < 0.05) higher prevalence of L. monocytogenes of 62% versus 17% by the culture-based method. Both methods revealed higher prevalences in the rural watershed and during the fall and winter seasons. Elevated Escherichia coli (≥ 100 CFU/100 ml) levels were not associated with the pathogen regardless of the detection method. Isolation of Listeria spp. were associated with 70 times higher odds of isolating L. monocytogenes (odds ratio = 70; P < 0.001). Serogroup IIa was predominant (67.7%) among the 285 L. monocytogenes isolates, followed by IVb (16.1%), IIb (15.8%), and IIc (0.4%). L. monocytogenes was detected in cow feces and raw sewage but not in septic tank samples. Pulsotyping of representative water (n = 54) and local human (n = 19) isolates suggested genetic similarities among some environmental and human L. monocytogenes isolates. In conclusion, temperate surface waters contain a diverse Listeria species population and could be a potential reservoir for L. monocytogenes, especially in rural agricultural watersheds.
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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 | 0.001 | 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) | 0.000 | 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".