Phenotypic and Genotypic Characteristics of Shiga Toxin-Producing Escherichia coli Isolated from Surface Waters and Sediments in a Canadian Urban-Agricultural Landscape
Bibliographic record
Abstract
A hydrophobic grid membrane filtration-Shiga toxin immunoblot method was used to examine the prevalence of Shiga toxin-producing Escherichia coli (STEC) in four watersheds located in the Lower Mainland of British Columbia, Canada, a region characterized by rapid urbanization and intensive agricultural activity. STEC were recovered from 21.6, 23.2, 19.5, and 9.2% of surface water samples collected monthly from five sites in each watershed over a period of 1 year. Overall prevalence was subject to seasonal variation however, ranging between 13.3% during fall months and 34.3% during winter months. STEC were also recovered from 23.8% of sediment samples collected in one randomly selected site. One hundred distinct STEC isolates distributed among 29 definitive and 4 ambiguous or indeterminate serotypes were recovered from water and sediments, including isolates from Canadian "priority" serogroups O157 (3), O26 (4), O103 (5), and O111 (7). Forty seven isolates were further characterized by analysis of whole genome sequences to detect Shiga toxin gene (stx 1 and stx 2), intimin gene (eaeA) allelic variants and acquired virulence factors. These analyses collectively showed that surface waters from the region support highly diverse STEC populations that include strains with virulence factors commonly associated with human pathotypes. The present work served to characterize the microbiological hazard implied by STEC to support future assessments of risks to public health arising from non-agricultural and agricultural uses of surface water resources in the region.
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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 | 0.002 |
| 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".