Temporal trends in <i>Giardia</i> occurrence in the Grand River and surrounding tributaries, Waterloo, Ontario (2005–2013), a retrospective analysis of surveillance data
Bibliographic record
Abstract
Giardia contamination in the Grand River Watershed (south-western Ontario, Canada) was monitored from 2005 to 2013 as part of FoodNet Canada. Our study objectives were to describe the temporal pattern of Giardia occurrence and determine whether water quality parameters and bacterial indicators could act as effective markers for Giardia occurrence. Water samples were collected monthly from the Grand River near a drinking water intake point (2005-2013) and also collected intermittently from other areas in the watershed during the study period. Samples were tested for Giardia cysts using the US EPA method 1623. Samples were also tested for chemical and microbial water quality indicators. Univariable and multivariable linear regression models were built to examine whether temporal, water quality and bacterial indicators were associated with Giardia cyst concentration. Giardia cysts were identified in 89% of samples (n = 228), with highest measured concentrations downstream of a waste water treatment plant outfall. Year and season were found to be predictors for Giardia occurrence. Concentrations were significantly higher in the winter and fall compared to the summer, and significantly higher in 2007 compared to other study years. After controlling for season, year and sampling location, dissolved oxygen was the only variable significantly associated with Giardia cyst concentration. Seasonal peaks in Giardia cyst concentrations in samples collected near the intake for the drinking water plant did not align with the seasonal peak in human Giardiasis cases in this region that are reported annually by public health authorities. This suggests that the risk of contracting Giardiasis from treated drinking water in this community is possibly low when the treatment plant is functioning adequately. Instead, waterborne exposure is likely the result of seasonal behaviours surrounding recreational water use. Therefore, the collective findings of our study are important to help inform future risk management studies and guide public health protection policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".