Tracking mercury sources in the Wabigoon River: Use of stable mercury isotopes in bioindicator organisms
Bibliographic record
Abstract
Mercury concentrations remain elevated in sediments and biota of the Wabigoon River downstream from Dryden, Ontario, the home of a former chlor-alkali plant. Understanding the current extent and severity of mercury contamination downstream of this industrial legacy site is of great importance in managing the mercury contamination within the traditional territory of Asubpeeschoseewagong Anishinabek (Grassy Narrows First Nation), located downstream of Dryden. The objective of this study was to use mercury stable isotope ratio analysis to distinguish between legacy mercury from the former chlor-alkali plant and mercury from geogenic sources. Mercury concentrations in surface sediments and biota downstream of the historical source of mercury discharge are elevated relative to the chosen reference location, Wabigoon Lake (WL). Mean sediment mercury levels were as high as 3.27 μg/g at the hydroelectric dam location compared to 0.05–0.10 μg/g at Wabigoon Lake sediments. Isotope ratios in aquatic biota and sediments collected from within the system were distinct from Wabigoon Lake, indicating that anthropogenic mercury contamination is distinguishable from geogenic mercury. Average δ 202 Hg values of −2.46 ± 0.41 observed in sediments of WL were consistently more negative compared to downstream values, which varied from −1.34 to 0.30 ‰. Young-of-the-year Yellow Perch and Hexagenia were found to have significantly more positive δ 202 Hg values downstream from Wabigoon Lake. • The project was able to distinguish between industrial mercury and geogenic mercury within local sediments. • Yellow Perch and Hexagenia, both popular bioindicator organisms used for mercury tracking was successful as species for tracking mercury stable isotopes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".