Metals in the drinking water of First Nations across Canada
Bibliographic record
Abstract
Abstract Objectives The First Nations Food, Nutrition and Environment Study (FNFNES), a community-based participatory research project, measured the levels of metals of health concern and the levels of metals that have operational guidance (OG) and aesthetic objectives (AO) in drinking water of First Nations (FN) south of the 60 th parallel. Methods Both stagnant (first draw) and flushed tap water samples were collected from participating households in 91 FN representing 11 ecozones. The concentrations of metals were quantified and compared to Health Canada’s Guidelines for Canadian Drinking Water Quality (GCDWQ). Results In total, 1516 FN households participated in this study component. Exceedances of the 2019 GCDWQ for the health-based maximum acceptable concentration (MAC) were found for five metals: lead 8.4% of households (first draw), manganese 4.0%, uranium 1.6%, aluminum 1.3%, and copper 0.2% (flushed). Flushing taps resulted in a decrease to 0.7% of households exceeding the lead MAC. Exceedances of the current OG for aluminum were found in 14.2% and the exceedances of the proposed OG were found in 18.1% of households (flushed). Exceedances of the AO (flushed) were as follows: manganese 12.8%, sodium 5.1%, iron 3.5%, and copper 0.4%. Results of the study were compared to provincial surveys where the data were available. Taste and colour were reported to be the main reasons for limiting the use of tap water. Conclusion Overall, the quality of drinking water with respect to trace metals of human health concern is satisfactory. However, elevated lead levels were found in some FN communities. Until appropriate action can take place, it was recommended to flush the water before use to reduce the lead levels.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".