Application of water safety planning to improve drinking water safety in an Arctic community – a case study in Cambridge Bay, Nunavut
Bibliographic record
Abstract
Water safety planning is a risk management approach that accounts for quantitative and qualitative drinking water hazards and includes ongoing input from stakeholders. This approach has been applied in jurisdictions across the world including Canada. Rural and remote communities in Canada, impacted by water safety, stand to benefit most from holistic approaches to water safety risk management such as water safety planning. Unfortunately, these communities typically have limited resources to engage in this approach. Additionally, most remote communities rely on truck and cistern water systems, which have less understood hazards than communities in Canada with piped service. In this study, we report the results of an initial water safety planning case study in Cambridge Bay, Nunavut. We identified numerous water quality hazards including disinfection byproducts in trucks, manganese in the source water, and copper in tap water, as well as operational challenges that increase the risk of water emergencies in the community. We conclude that water safety planning has the potential to substantially improve water safety in Nunavut but current information gaps as well as complex stakeholder interactions are likely to hinder top-down attempts. A dynamic and inclusive approach is recommended that incorporates a targeted exploration of water safety hazards.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".