Potential Health Risks of Contaminants in Private Groundwater Sources in Newfoundland and Labrador
Bibliographic record
Abstract
The Government of Newfoundland and Labrador regularly tests public drinking water supplies to ensure the absence of any microbiological, physical or chemical contaminants.Private water supplies, including wells however, fall outside the mandate of these testing regimes and thus monitoring becomes the sole responsibility of the individual well owner.There are over 40,000 wells in Newfoundland and Labrador servicing approximately one fifth of the total population.Limited information on private well water quality is available, especially of physical and chemical contaminants.A scan of provincial government water quality reports of public wells was performed to create a proxy model of the potential risk of private well contamination.Our results show potential problems with toxic levels of arsenic, barium, cadmium, chromium, lead, mercury and selenium.Lead and arsenic pose the greatest risk with 13% and 10% of public wells having shown contamination at least once.Our model finds 8,544 people at risk for exposure to toxic levels of arsenic, 11,232 people at risk of exposure to toxic levels of lead, and 3,840 people at risk for exposure to the remaining contaminants from drinking water from private wells.In total, this model shows 5% of the province's population at risk of exposure to toxic drinking water contaminants.A review of the literature was conducted to assess the health risks from each of these individual contaminants.Health risks on account of chronic exposure to these seven contaminants include cancer, cardiovascular disease, kidney damage, diabetes, as well as neurological and developmental conditions, among others.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".