Identifying host sources, human health risk and indicators of Cryptosporidium and Giardia in a Canadian watershed influenced by urban and rural activities
Bibliographic record
Abstract
Cryptosporidium and Giardia were characterized in a watershed in southern Ontario, Canada, over a 2½ year period. River samples were collected every two weeks, primarily near a municipal drinking water treatment plant intake. Cryptosporidium and Giardia were frequently detected with an overall occurrence rate of 88 and 97%, respectively. Giardia concentrations were higher than Cryptosporidium, with median values of 80 cysts 100 L(-1) and 12 oocysts 100 L(-1), respectively. Although pathogens rarely show a significant relationship with fecal or water quality indicators, this study determined that Cryptosporidium, but not Giardia, was significantly correlated with Escherichia coli, turbidity and river flow. There was no correlation between the two types of protozoa, and only Giardia showed a seasonal trend with higher concentrations at cold water temperatures. Cryptosporidium genotyping of all samples found that farm animals and wildlife were an important contributor of oocysts in the watershed, and that Cryptosporidium strains/genotypes of medium to high risk for human infection (C. hominis, C. parvum and C. ubiquitum) were detected in 16% of samples. This study was able to identify Cryptosporidium host sources and human health risk, and to identify differences between Cryptosporidium and Giardia occurrence in the watershed.
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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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".