Global Meta‐Analysis on the Relationship Between Mercury and Dissolved Organic Carbon in Freshwater Environments
Bibliographic record
Abstract
Abstract In freshwater ecosystems, several studies have shown a strong linear relationship between total mercury (THg) or methylmercury (MeHg) and dissolved organic carbon (DOC) concentrations. Variations in this linear relationship have been reported, but the magnitude and causes of this variation are not well known. The objective of this study was to conduct a meta‐analysis to quantify and understand the global variation of this mercury (Hg)–DOC association. This meta‐analysis included 54 studies in lentic and lotic ecosystems for a total of 85 THg–DOC and 59 MeHg–DOC relationships. There was an increase in Hg with DOC concentrations in water with a global average slope of 0.25 (confidence interval (CI): 0.20–0.35) ng/mg for THg and 0.029 (CI: 0.014–0.044) ng/mg for MeHg. Relationships were stronger for (1) North American studies, (2) natural environments compared to those disturbed by anthropogenic activities, (3) spatial studies compared to temporal studies, (4) filtered samples (THg only), and (5) the aromatic fraction of DOC compared to the bulk DOC. Coupling with DOC was stronger for THg than for MeHg. Ecosystem type (lentic vs. lotic), geographical coordinates, and publication year did not influence the strength of relationships. Overall, we show that there is a strong but variable coupling between carbon and mercury cycles in freshwater ecosystems globally and that this link is modulated regionally by geographic location, temporal scale, and human activity, with implications for understanding these rapidly changing biogeochemical processes in response to global change.
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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 | 0.017 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.035 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".