Prevalence of<i>Escherichia coli</i>O157:H7 and<i>Salmonella</i>spp. in surface waters of southern Alberta and its relation to manure sources
Bibliographic record
Abstract
The Oldman River watershed in southern Alberta, Canada, is an extensively irrigated region in which intensive agricultural practices have flourished. Concern over water quality in the basin has been expressed because of high levels of enteric disease indigenous to the region. To address these concerns, we conducted a 2-year study to estimate the prevalence of Escherichia coli O157:H7 and Salmonella spp. in surface water within the basin. This study is the first of its kind to identify E. coli O157:H7 repeatedly in surface water collected from a Canadian watershed. Prevalence of E. coli O157:H7 and Salmonella spp. in water samples was 0.9% (n = 1,483) and 6.2% (n = 1,429), respectively. While data examined at a regional level show a relationship between high livestock density and high pathogen levels in southern Alberta, statistical analysis of point source data indicates that predicted manure output from bovine, swine, and poultry feeding operations was not directly associated with either Salmonella spp. or E. coli O157:H7 prevalence. However, geography and weather variables, which are likely to influence bacterial runoff, were not considered in this model. We also postulate that variations in time, amount, and frequency of manure application onto agricultural lands may have influenced levels of surface-water contamination with these bacterial pathogens.
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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 | 0.002 |
| 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) | 0.001 | 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".