Mercury Methylation Potentials in Water and Sediments in the Wabigoon River System
Bibliographic record
Abstract
The Wabigoon River is known for an historic mercury (Hg) pollution source, caused by a chlor-alkali facility operating in the 60's.The legacy Hg contamination persists to current days, causing adverse health effects to local communities.Methylmercury (MMHg) is one of the most toxic mercury species due to its potential for bioaccumulation in the food chain, attaining its highest concentrations in the tissues of top predatory fish due to biomagnification.These contaminated fish are the main pathway for mercury exposure in humans.That said, the tendency of an environment to produce methylmercury from inorganic mercury is important in determining the potential impact of Hg on human health and the environment.In order to understand which areas within the system are impacting negatively the local communities, mercury stable isotope tracers were used to assess Hg methylation at different locations along the river system, including lake, river and wetland sediments as well as water.Hotspots for methylmercury formation were found to be at the Hydro dam and Clay Lake locations, where up to 4.4 % and 3.8 % of the added mercury spike were methylated, respectively.These locations have the potential to produce and distribute methylmercury throughout the Wabigoon River system.This work establishes for the first time, Hg methylation potentials in several ecosystems across the Wabigoon River System, discerning risk areas for MMHg production.
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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 | 0.001 | 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) | 0.001 | 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".