Detection of fecal bacteria and antibiotic resistance genes in drinking water collected from three First Nations communities in Manitoba, Canada
Bibliographic record
Abstract
This study analyzed the microbiological quality of drinking and source water from three First Nations communities in Manitoba, Canada that vary with respect to the source, storage and distribution of drinking water. Community A relies on an aquifer and Community B on a lake as source water to their water treatment plants. Community C does not have a water treatment plant and uses well water. Quantification of free residual chlorine and fecal bacterial (E. coli and coliforms), as well as detection of antibiotic resistance genes (sul, ampC, tet(A), mecA, vanA, blaSHV, blaTEM, blaCTX-M, blaOXA-1, blaCYM-2, blaKPC, blaOXA-48, blaNDM, blaVIM, blaGES and blaIMP) was carried out. While water treatment plants were found to be working properly, as post-treatment water did not contain E. coli or coliforms, once water entered the distribution system, a decline in the chlorine concentration with a concomitant increase in bacterial counts was observed. In particular, water samples from cisterns not only contained high number of E. coli and coliforms, but were also found to contain antibiotic resistance genes. This work shows that proper maintenance of the distribution and storage systems in First Nations communities is essential in order to provide access to clean and safe drinking water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".