Informing Giardia surveillance and giardiasis prevention in a FoodNet Canada sentinel site through an investigation of local surface waters and risk factors associated with domestic and foreign travel
Bibliographic record
Abstract
Two studies were conducted to explore source attribution of human giardiasis cases in the Region of Waterloo, Ontario by examining local watershed contamination with Giardia cysts and identifying differences in exposures to risk factors in cases with travel history compared to endemic cases. Linear regression models were constructed to identify temporal trends in water contamination with Giardia, as well as water quality parameters that may indicate high cyst levels. Logistic regression models were constructed to determine if there were significant differences in risk behaviours among international travel related giardiasis cases compared to Canadian acquired cases. Multinomial regression models were then constructed to determine significant differences in risk behaviours between international travel related cases, domestic travel related cases, and endemic cases of giardiasis. Giardia cyst concentrations peak in the winter and spring months in the Grand River and river discharge, turbidity, conductivity, phosphate concentration and phosphorous concentration were significantly associated with Giardia cyst concentrations. Results from both logistic and multinomial regression models indicated that risk behaviours differ significantly between case types and the traditional grouping of domestic travel related cases and endemic cases into a Canadian acquired case group may not be appropriate. These results are important for future Giardia surveillance, source attribution, informing policy makers, and effectively targeting health promotion campaigns for reducing human giardiasis in this 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".