Archived government correspondence reveals extreme arsenic pollution of local waterbodies from gold mining at Yellowknife, NT prior to environmental regulation
Bibliographic record
Abstract
The Yellowknife area was one of the most productive and profitable gold districts in Canadian history. The early years of operation were associated with large releases of mining waste to local water bodies that have resulted in an enduring environmental legacy in the region. Here, we compile, for the first time, archival information on the scale of impact to local waterbodies during the highest environmental emissions (1949–1956). More than 800 measurements of arsenic (As) concentrations from local waterbodies, ponded surface water, and domestic water sources were extracted from archived government documents during this period. The compilation of these data revealed extreme and widespread arsenic contamination of local waterbodies from mining operations with concentrations up to 47 000 µg L−1 As. The archived correspondence included documentation of public health effects revealing that local mining emissions were a public health risk during this period. Comparison of the archived water quality records with contemporary data for the same lakes indicated widespread reductions of lake water arsenic concentrations across the region over half a century. These data provide evidence of the extent of historical environmental impacts of mining on Indigenous territory and of government and industry failure to stop emissions when faced with evidence that local mines were causing environmental pollution and risks to public health. By bringing these data to light, it is hoped that this information will support reconciliation efforts between the federal and territorial governments and local Indigenous Peoples.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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".