Using Host-Specificity of <i>Cryptosporidium</i> to Understand Contaminant Sources, Seasonality, and Human Health Risk in Three Watersheds of Differing Land-Use
Bibliographic record
Abstract
Three tributaries of the Grand River watershed (Ontario, Canada), each representing different watershed types (urban, agricultural/rural, and mixed land-use) were examined to understand the spatial, temporal, and host-source distribution of the waterborne pathogen, Cryptosporidium. Cryptosporidium was frequently found throughout the study (73%, 65/89) with occurrence and concentrations observed to be similar among the varying watershed types. However, applying advanced genotyping techniques, marked differences in dominant host sources could be observed in each watershed. The agricultural/rural and mixed land-use watersheds were dominated by genotypes typically associated with cattle (i.e., C. andersoni), while the urban watershed had the highest diversity of Cryptosporidium genotypes with a variety of wildlife as the common source of contamination (e.g., muskrat and cervine genotypes). A similar seasonal trend observed in the urban, agricultural, and mixed land-use watershed suggests that factors beyond specific land use activities (e.g. autumn manure spreading) may influence the timing and concentration of Cryptosporidium in these streams. Corresponding genotyping results provided additional insight into source inputs during these seasonal peaks, indicating that wildlife may be important seasonal contributors to Cryptosporidium contamination in these streams. Despite the abundance of Cryptosporidium in these watersheds, most of the genotypes observed were of limited human health importance. This study provides evidence regarding the significance of including genotyping results into studies examining waterborne Cryptosporidium. Using this technique can provide a greater understanding of the risk to the population using water sources, as well as provide insight into the probable sources and timing of contamination. This ancillary information can contribute to implementation of targeted management strategies to further protect sources of drinking water and recreation areas.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".