Mercury isotope compositions across North American forests
Bibliographic record
Abstract
Abstract Forest biomass and soils represent some of the largest reservoirs of actively cycling mercury (Hg) on Earth, but many uncertainties exist regarding the source and fate of Hg in forest ecosystems. We systematically characterized stable isotope compositions of Hg in foliage, litter, and mineral soil horizons across 10 forest sites in the contiguous United States. The mass‐independent isotope signatures in all forest depth profiles are more consistent with those of atmospheric Hg(0) than those of atmospheric Hg(II), indicating that atmospheric Hg(0) is the larger source of Hg to forest ecosystems. Within litter horizons, we observed significant enrichment in Hg concentration and heavier isotopes along the depth, which we hypothesize to result from additional deposition of atmospheric Hg(0) during litter decomposition. Furthermore, Hg isotope signatures in mineral soils closely resemble those of the overlying litter horizons suggesting incorporation of Hg from litter as a key source of soil Hg. The spatial distribution of Hg isotope compositions in mineral soils across all sites is modeled by isotopic mixing assuming atmospheric Hg(II), atmospheric Hg(0), and geogenic Hg as major sources. This model shows that northern sites with higher precipitation tend to have higher atmospheric Hg(0) deposition than other sites, whereas drier sites in the western U.S. tend to have higher atmospheric Hg(II) deposition than the rest. We attribute these differences primarily to the higher litterfall Hg input at northern wetter sites due to increased plant productivity by precipitation. These results allow for a better understanding of Hg cycling across the atmosphere‐forest‐soil interface.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".