Insights into water insecurity in Indigenous communities in Canada: assessing microbial risks and innovative solutions, a multifaceted review.
Bibliographic record
Abstract
While most Canadians have access to high-quality drinking water, several Indigenous reserves face water insecurity. Drinking water systems (DWS) on reserves face limitations ranging from aging infrastructure and shared administration of water regulations. When potential hazards are identified in source waters, local environmental authorities may issue “water advisories”. Up to date, more than 20 long-term water advisories remain unresolved in Indigenous reserves in Canada. The risks associated with water insecurity include the presence of pathogenic microorganisms (i.e. Escherichia coli and total coliforms) and the reaction of natural organic matter (NOM) with disinfection chemicals in the DWS potentially forming disinfection by-products (DBPs). We revised the challenges and the potential use of different methods to remove NOM from water including coagulation, high- and low-pressure membrane filtration procedures, ozone, Ion exchange (IEX), and Biological Ion exchange (BIEX). Moreover, we reviewed the benefits and drawbacks that high throughput tools such as metagenomics, culturomics, and microfluidics devices could represent for water monitoring in Indigenous reserves. This review pursues a better understanding of the microbiological and chemical risks that water insecurity causes in Indigenous reserves in Canada. Additionally, we evaluate the potential implications of the potential technical and microbiological solutions that can be used to prevent the effect of pathogens in water and protect public health in Indigenous reserves.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".