Perchlorate in Lake Water from an Operating Diamond Mine
Bibliographic record
Abstract
Mining-related perchlorate [ClO4(-)] in the receiving environment was investigated at the operating open-pit and underground Diavik diamond mine, Northwest Territories, Canada. Samples were collected over four years and ClO4(-) was measured in various mine waters, the 560 km(2) ultraoligotrophic receiving lake, background lake water and snow distal from the mine. Groundwaters from the underground mine had variable ClO4(-) concentrations, up to 157 μg L(-1), and were typically an order of magnitude higher than concentrations in combined mine waters prior to treatment and discharge to the lake. Snow core samples had a mean ClO4(-) concentration of 0.021 μg L(-1) (n=16). Snow and lake water Cl(-)/ClO4(-) ratios suggest evapoconcentration was not an important process affecting lake ClO4(-) concentrations. The multiyear mean ClO4(-) concentrations in the lake were 0.30 μg L(-1) (n = 114) in open water and 0.24 μg L(-1) (n = 107) under ice, much below the Canadian drinking water guideline of 6 μg L(-1). Receiving lake concentrations of ClO4(-) generally decreased year over year and ClO4(-) was not likely [biogeo]chemically attenuated within the receiving lake. The discharge of treated mine water was shown to contribute mining-related ClO4(-) to the lake and the low concentrations after 12 years of mining were attributed to the large volume of the receiving lake.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".