Benefits-risks analysis of drinking water systems in Small and Rural Indigenous (SRI) communities in Canada
Bibliographic record
Abstract
Small and Rural Indigenous (SRI) communities face significant barriers, including geographic isolation, inadequate infrastructure, and financial constraints, which limit their access to safe drinking water. Chemical pollutants from natural and anthropogenic sources contribute to water pollution and in some cases may be linked to numerous cancer (CR) and non-cancer risks (NCR). Improving water systems (WSs) can reduce disease prevalence and enhance public health and community well-being. These improvements may also lead to higher water quality perception and potential economic returns. Therefore, evaluating the risks and benefits of WSs is critical. This study developed a risk management framework to identify and mitigate water quality risks in SRI communities. The framework was developed in three phases and was tested on six SRI communities in Canada. Pollutants such as arsenic, fluoride, and manganese were considered as their occurrence and linkage to chronic and acute health effects. Overall, the larger community (WS5) has 31.63 % more benefits on average compared to the smaller communities. Higher levels of fluoride and manganese primarily drove the benefit-to-risk ratio (BRR) of WS4, while elevated arsenic levels influenced the BRR of WS6. Additionally, the BRR was influenced by the communities’ perception of water quality, which was relatively low, with only 60 % of residents satisfied. The proposed methodology and the demonstrated case studies provide valuable guidance for conducting future research and developing effective water management practices for SRI communities across Canada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".