Mercury and methylmercury in aquatic sediment across western North America
Bibliographic record
Abstract
Large-scale assessments are valuable in identifying primary factors controlling total mercury (THg) and monomethyl mercury (MeHg) concentrations, and distribution in aquatic ecosystems. Bed sediment THg and MeHg concentrations were compiled for >16,000 samples collected from aquatic habitats throughout the West between 1965 and 2013. The influence of aquatic feature type (canals, estuaries, lakes, and streams), and environmental setting (agriculture, forest, open-water, range, wetland, and urban) on THg and MeHg concentrations was examined. THg concentrations were highest in lake (29.3±6.5μgkg(-1)) and canal (28.6±6.9μgkg(-1)) sites, and lowest in stream (20.7±4.6μgkg(-1)) and estuarine (23.6±5.6μgkg(-1)) sites, which was partially a result of differences in grain size related to hydrologic gradients. By environmental setting, open-water (36.8±2.2μgkg(-1)) and forested (32.0±2.7μgkg(-1)) sites generally had the highest THg concentrations, followed by wetland sites (28.9±1.7μgkg(-1)), rangeland (25.5±1.5μgkg(-1)), agriculture (23.4±2.0μgkg(-1)), and urban (22.7±2.1μgkg(-1)) sites. MeHg concentrations also were highest in lakes (0.55±0.05μgkg(-1)) and canals (0.54±0.11μgkg(-1)), but, in contrast to THg, MeHg concentrations were lowest in open-water sites (0.22±0.03μgkg(-1)). The median percent MeHg (relative to THg) for the western region was 0.7%, indicating an overall low methylation efficiency; however, a significant subset of data (n>100) had percentages that represent elevated methylation efficiency (>6%). MeHg concentrations were weakly correlated with THg (r(2)=0.25) across western North America. Overall, these results highlight the large spatial variability in sediment THg and MeHg concentrations throughout western North America and underscore the important roles that landscape and land-use characteristics have on the MeHg cycle.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".