Perchlorate\nin Lake Water from an Operating Diamond\nMine
Bibliographic record
Abstract
Mining-related perchlorate\n[ClO<sub>4</sub><sup>–</sup>]\nin the receiving environment was investigated at the operating open-pit\nand underground Diavik diamond mine, Northwest Territories, Canada.\nSamples were collected over four years and ClO<sub>4</sub><sup>–</sup> was measured in various mine waters, the 560 km<sup>2</sup> ultraoligotrophic\nreceiving lake, background lake water and snow distal from the mine.\nGroundwaters from the underground mine had variable ClO<sub>4</sub><sup>–</sup> concentrations, up to 157 μg L<sup>–1</sup>, and were typically an order of magnitude higher than concentrations\nin combined mine waters prior to treatment and discharge to the lake.\nSnow core samples had a mean ClO<sub>4</sub><sup>–</sup> concentration\nof 0.021 μg L<sup>–1</sup> (n=16). Snow and lake water\nCl<sup>–</sup>/ClO<sub>4</sub><sup>–</sup> ratios suggest\nevapoconcentration was not an important process affecting lake ClO<sub>4</sub><sup>–</sup> concentrations. The multiyear mean ClO<sub>4</sub><sup>–</sup> concentrations in the lake were 0.30 μg\nL<sup>–1</sup> (<i>n</i> = 114) in open water and\n0.24 μg L<sup>–1</sup> (<i>n</i> = 107) under\nice, much below the Canadian drinking water guideline of 6 μg\nL<sup>–1</sup>. Receiving lake concentrations of ClO<sub>4</sub><sup>–</sup> generally decreased year over year and ClO<sub>4</sub><sup>–</sup> was not likely [biogeo]chemically attenuated\nwithin the receiving lake. The discharge of treated mine water was\nshown to contribute mining-related ClO<sub>4</sub><sup>–</sup> to the lake and the low concentrations after 12 years of mining\nwere attributed to the large volume of the receiving lake.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.905 | 0.042 |
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; both teacher heads agree on what is shown here.
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".