Relevance and effectiveness of best management practices to mitigate cryptosporidium contamination of drinking water from community watersheds in British Columbia by grazing cattle
Bibliographic record
Abstract
The literature has shown that grazing cattle can potentially contaminate surface drinking water with Cryptosporidium parvum (C. parvum), a public health concern. In 2010, an audit by the British Columbia’s (BC) Forest Practices Board sampled cattle faeces in Oyama Creek watershed that tested positive for Cryptosporidium. In response, stakeholders piloted six best management practices (BMPs) to mitigate the perceived risk to human health, including access scheduling, exclusion fencing, controlled access to water, off-stream watering, key area management, and silvopasture. A review of the literature revealed that these practices should avert human health risks. To verify BMP effectiveness, water, cattle faeces, and sediment samples were collected from May to October during 2013, 2014, and 2015, and analyzed for Cryptosporidium presence and Escherichia coli. Cryptosporidium positive samples were sequenced to determine species. The maximum concentration of Cryptosporidium oocysts detected was 17.1 oocyst/l in an ephemeral tributary to Vernon Creek and 12.9 oocyst/l at the Oyama Creek intake, both well below what Canadian drinking water treatment systems are designed to remove. Successfully sequenced samples were identified as C. andersoni, which is not generally problematic to human health. This indicates that normal BC ranching practices mitigate the hazard even without BMPs. E.coli presence did not correlate with Cryptosporidium presence. Of the BMPs tested, access scheduling, keeping young calves away from vulnerable streams seems very effective. Exclusion fences and key area management are also effective. Controlled access watering as implemented proved problematic. No conclusions about off-stream watering or silvopasture could be made on the basis of the samples collected. BC Centre for Disease Control (BCCDC) provided data for 56 cryptosporidiosis incidences from 2005 to 2014 in the nine Local Health Reporting Areas (LHAs) of the Okanagan Health Services Delivery Area (HSDA). The presence of grazing cattle in watersheds did not correlate with increased incidences of cryptosporidiosis in LHAs. The conclusion from both the field and lab research, and the analysis of the BCCDC data is that grazing cattle in community watersheds in BC are not the risk to human health for cryptosporidiosis they might be perceived to be.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".