Temporal variation in the prevalence and species richness of <i>Campylobacter</i> spp. in a prairie watershed impacted by urban and agricultural mixed inputs
Bibliographic record
Abstract
Campylobacter spp. are a substantial cause of gastroenteritis worldwide. Human infection can result from ingestion of contaminated food or water from a variety of sources, including the consumption of fresh produce that is contaminated with the pathogen via the use of contaminated irrigation water. Using molecular methods, we investigated the occurrence of Campylobacter in the Qu'Appelle River watershed, an important source of irrigation water for vegetable producers in southern Saskatchewan, Canada. Water samples were collected from 7 sampling sites from April to September 2009 (145 samples), and from 5 sampling sites from May to October 2013 (116 samples). Campylobacter was detected in 57% and 16% of the samples collected in 2009 and 2013, respectively. Campylobacter detection was highest in May and June for both sampling years. In 2009, the predominant species were Campylobacter lari and Campylobacter jejuni, with prevalences of 84% and 41%, respectively. Other Campylobacter spp. were detected less frequently. Only C. lari was detected in 2013. The results in 2009 demonstrate the species richness of Campylobacter in water sources within the watershed. The occurrence of Campylobacter in the study area also underscores the importance of monitoring irrigation water used to irrigate fresh produce from a public health prospective.
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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 | 0.001 | 0.001 |
| 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".