Organic matter, eutrophication, and increased mass accumulation rates in the Anthropocene are the main drivers of mercury concentrations and historical enrichment in Canadian lake sediments
Bibliographic record
Abstract
Abstract Mercury (Hg) contamination in the environment is a persistent issue as emissions from industry are ongoing and legacy cycling is prolonged. To quantify total mercury (total Hg) and methylmercury (MeHg) concentrations in lake sediments across a wide suite of temperate to subarctic lakes, measurements and modeling were conducted for 320 sites across Canada. Total Hg varied from 7 to 567 ng g−1 dry sediment with a mean (median) value of 148 (128) ng g−1 dry sediment, with lowest concentrations observed in eutrophic lakes of the Prairies and Boreal Plains. Average total Hg concentrations have increased twofold in contemporary sediments relative to their preindustrial pair. MeHg concentrations were generally low, with a mean of 0.8 ng g−1 dry sediment and this form representing on average 0.6% of the total contemporary mercury concentrations. Water column variables related to eutrophication were among the strongest predictors of contemporary total Hg, MeHg, and total Hg enrichment in the sediments. In lakes where sediment radiometric data were collected, contemporary total Hg accumulation rates were strongly related to longitude(+) and Hg atmospheric deposition(+). Latitude(−) and Hg atmospheric deposition(+) were the best predictors of MeHg accumulation rates. Chromophoric dissolved organic matter optical properties were key predictors of both total Hg concentrations and accumulation rates. Our work highlights the paramount importance of lake trophic state and the nature of organic matter as key predictors of lake sediment mercury metrics.
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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 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".