A review on arsenic concentrations in Canadian drinking water
Bibliographic record
Abstract
Recent events have increased public awareness of drinking water quality in Canada. The goal of this review was to examine how much information about arsenic (As) in Canadian drinking water is available. Provincial, territorial, and federal Web sites were searched for information about As in drinking water. Major scientific databases (PubMed, Web of Science) were searched for drinking water As information for all provinces and territories. Resulting information was examined for availability, accessibility, quality, and timeliness. Most provinces provided at least basic fact sheets about As, and several provinces provided comprehensive databases containing actual test results. The vast majority of Canadian municipal drinking water systems with As data show a concentration below 10 μg/L, the current guideline level. Several locations in Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland and Labrador, Nova Scotia, Québec, and Saskatchewan have localized elevations of As (“hotspots”, >10 μg/L As); this information is available at the provincial level, but may not include exact locations nor the degree by which they exceed the current guideline limit of 10 μg/L. For other locations, however, little information is available. The lack of a centralized information source represents a significant obstacle to obtaining drinking water quality data. Although difficult to implement, a centralized and standardized source of national drinking water quality data is urgently needed to determine the effects of As and other contaminants on Canadians.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.019 |
| 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) | 0.005 | 0.001 |
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".