Evidence of Long-Range Transport of Selenium Downstream of Coal Mining Operations in the Elk River Valley, Canada
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Expanding coal-mining operations in the Elk River Valley (British Columbia, Canada) have increased total selenium (Se) concentrations in the transboundary Lake Koocanusa (Canada and United States), but the spatial extent of Se transport from the Elk River Mines is unknown. We evaluated multiple lines of evidence of long-range transport of Se at five sites downstream of the mines relative to a site unaffected by the mines. First, all mine-affected sites had increasing trends in flow-normalized Se concentrations between 2005 and 2021 (35–89%), while no trend was observed at the unaffected site between 2005 and 2017. Second, all mine-affected sites had elevated annual mean Se concentrations and 5-year mean annual loads (2013–2017) by up to an order of magnitude relative to the unaffected site. Third, Se concentrations and the magnitude of the concentration trend generally decreased with distance downstream from the mines while loads increased, which is consistent with the downstream transport of the bulk of the Se load from the mines with smaller contributions from other sources. These results provide multiple lines of evidence that Se from the Elk River Mines is transported over 575 river kilometers and may pose risks to aquatic life in the transboundary Columbia River.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".