Boiling over: A Descriptive Analysis of Drinking Water Advisories in First Nations Communities in Ontario, Canada
Bibliographic record
Abstract
Access to safe and reliable drinking water is commonplace for most Canadians. However, the right to safe and reliable drinking water is denied to many First Nations peoples across the country, highlighting a priority public health and environmental justice issue in Canada. This paper describes trends and characteristics of drinking water advisories, used as a proxy for reliable access to safe drinking water, among First Nations communities in the province of Ontario. Visual and statistical tools were used to summarize the advisory data in general, temporal trends, and characteristics of the drinking water systems in which advisories were issued. Overall, 402 advisories were issued during the study period. The number of advisories increased from 25 in 2004 to 75 in 2013. The average advisory duration was 294 days. Most advisories were reported in summer months and equipment malfunction was the most commonly reported reason for issuing an advisory. Nearly half of all advisories occurred in drinking water systems where additional operator training was needed. These findings underscore that the prevalence of drinking water advisories in First Nations communities is a problem that must be addressed. Concerted and multi-faceted efforts are called for to improve the provision of safe and reliable drinking water First Nations communities.
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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 | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".