Quantitative Assessment of First Nations Drinking Water Distribution Systems for Detection and Prevalence of Thermophilic Campylobacter Species
Bibliographic record
Abstract
Water is considered a major route for transmitting human-associated pathogens. Although microbial water quality indicators are used to test for the presence of waterborne pathogens in drinking water, the two are poorly correlated. The current study investigates the prevalence of thermophilic DNA markers specific for Campylobacter spp. (C. jejuni and C. coli) in source water and throughout the water distribution systems of two First Nations communities in Manitoba, Canada. A total of 220 water samples were collected from various points of the drinking water distribution system (DWDS) between 2016 and 2018. Target Campylobacter spp. were always (100%) detected in a home with a fiberglass (CF) cistern, as well as the community standpipe (SP). The target bacteria were also frequently detected in treated water at the Water Treatment Plant (WTP) (78%), homes with polyethylene (CP) (60%) and concrete (CC) (58%) cisterns, homes with piped (P) water (43%) and water truck (T) samples (20%), with a maximum concentration of 1.9 × 103 cells 100 mL−1 (C. jejuni) and 5.6 × 105 cells 100 mL−1 (C. coli). Similarly, target bacteria were detected in 68% of the source water samples with a maximum concentration of 4.9 × 103 cells 100 mL−1 (C. jejuni) and 8.4 × 105 cells 100 mL−1 (C. coli). Neither target Campylobacter spp. was significantly associated with free and total chlorine concentrations in water. The study results indicate that there is an immediate need to monitor Campylobacter spp. in small communities of Canada and, particularly, to improve the DWDS in First Nations communities to minimize the risk of Campylobacter infection from drinking water sources. Further research is warranted in improving/developing processes and technologies to eliminate microbial contaminants from water.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".