Predicting Behavior and Fate of Atmospheric Mercury in Soils: Age-Dating METAALICUS Hg Isotope Spikes with Fallout Radionuclide Chronometry
Bibliographic record
Abstract
Soils accumulate anthropogenic mercury (Hg) from atmospheric deposition to terrestrial ecosystems. However, possible reemission of gaseous elemental mercury (GEM) back to the atmosphere as well as downward migration of Hg with soil leachate influence soil sequestration of Hg in ways not sufficiently understood in global biogeochemical models. Here, we apply fallout radionuclide (FRN) chronometry to understand soil Hg dynamics by revisiting the METAALICUS experiments 20 years after enriched isotope tracers ( 198 Hg, 200 Hg, 201 Hg, and 202 Hg) were applied to two boreal watersheds in northwestern Ontario, Canada. Hg spikes formed well-defined peaks in organic horizons of both watersheds at depths of 3–6 cm and were accurately dated to the year of spike application in 6 of 7 cases (error = −0.8 ± 1.2 years). A seventh site was depleted by ca. 90% of both the 200 Hg spike and background Hg, and the spike was dated 16 years older than its application. Robust FRN age models and mass balances demonstrate that loss of Hg is attributable to its specific physicochemical behavior at this site, but more work is required to attribute this to reemission or leaching. This study demonstrates the potential of FRN chronometry to provide insights into Hg accumulation, mobilization, and fate in forest soils.
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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 | 0.001 | 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".