Estimating Potential Environmental Loadings of<i>Cryptosporidium</i>spp. and<i>Campylobacter</i>spp. from Livestock in the Grand River Watershed, Ontario, Canada
Bibliographic record
Abstract
Exposure to waterborne pathogens in recreational or drinking water is a serious public health concern. Thus, it is important to determine the sources of pathogens in a watershed and to quantify their environmental loadings. The natural variability of potentially pathogenic microorganisms in the environment from anthropogenic, natural, and livestock sources is large and has been difficult to quantify. A first step in characterizing the risk of nonpoint source contamination from pathogens of livestock origin is to determine the potential environmental loading based on animal prevalence and fecal shedding intensity. This study developed a probabilistic model for estimating the production of Cryptosporidium spp. and Campylobacter spp. from livestock sources within a watershed. Probability density functions representing daily pathogen production rates from livestock were simulated for the Grand River Watershed in southwestern Ontario. The prevalence of pathogenic microorganisms in animals was modeled as a mixture of beta-distributions with parameters drawn from published studies. Similarly, gamma-distributions were generated to describe animal pathogen shedding intensity. Results demonstrate that although cattle are responsible for the largest amount of manure produced, other domesticated farm animals contribute large numbers of the two pathogenic microorganisms studied. Daily pathogen production rates are highly sensitive to the parameters of the gamma-distributions, illustrating the need for reliable data on animal shedding intensity. The methodology may be used for identifying source terms for pathogen fate and transport modeling and for defining and targeting regions that are most vulnerable to water contamination from pathogenic sources.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| 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".