Reactivity and Mobility of New and Old Mercury Deposition in a Boreal Forest Ecosystem during the First Year of the METAALICUS Study
Bibliographic record
Abstract
The METAALICUS (Mercury Experiment To Assess Atmospheric Loading In Canada and the US) project is a whole ecosystem experiment designed to study the activity, mobility, and availability of atmospherically deposited mercury. To investigate the dynamics of mercury newly deposited onto a terrestrial ecosystem, an enriched stable isotope of mercury (202Hg) was sprayed onto a Boreal forest subcatchment in an experiment that allowed us, for the first time, to monitor the fate of 'new' mercury in deposition and to distinguish it from native mercury historically stored in the ecosystem. Newly deposited mercury was more reactive than the native mercury with respect to volatilization and methylation pathways. Mobility through runoff was very low and strongly decreased with time because of a rapid equilibration with the large native pool of "bound" mercury. Over one season, only approximately 8% of the added 212Hg volatilized to the atmosphere and less than 1% appeared in runoff. Within a few months, approximately 66% of the applied 202Hg remained associated with above ground vegetation, with the rest being incorporated into soils. The fraction of 202Hg bound to vegetation was much higher than seen for native Hg (<5% vegetation), suggesting that atmospherically derived mercury enters the soil pool with a time delay, after plants senesce and decompose. The initial mobility of mercury received through small rain events or dry deposition decreased markedly in a relatively short time period, suggesting that mercury levels in terrestrial runoff may respond slowly to changes in mercury deposition rates.
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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 | 0.000 |
| 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) | 0.000 | 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".