Factors Influencing Mercury in Freshwater Surface Sediments of Northeastern North America
Bibliographic record
Abstract
Abstract. We report on an inventory and analysis of sediment mercury (Hg) concentrations from 579 sites across northeastern North America. Sediment Hg concentrations ranged from the limit of detection ca. 0.01–3.7 lg g)1 (dry weight, d.w.), and the average concentration was 0.19 lg g)1 (d.w.) Sediment methylmercury concentrations ranged from 0.15 to 21 ng g)1 (d.w.) and the mean concentration was 3.83 ng g)1 (d.w.). Total Hg concentrations (HgT) were greatest in lakes> reservoirs> rivers, although the proportion of Hg as methylmercury showed an inverse pattern. Total Hg was weakly and positively correlated with the sediment organic matter and percent of watershed as forested land, and weakly and negatively correlated with sediment solids content, drainage area, and agricultural land. Sediment meth-ylmercury concentrations were weakly and positively correlated to wetland area, and weakly and negatively correlated to drainage area. Methylmercury, expressed as a percentage of HgT was positively correlated to agricultural land area. For sites with co-located sediment and fish-tissue sampling results, there was no relationship between sediment Hg and fish-tissue Hg. Finally, our data indicate that at least 44 % of waters across the region have sediment HgT concentrations in excess of Canadian and United States minimum sediment contaminant guidelines for the protection of aquatic biota.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".