A mass budget for mercury and methylmercury in the Arctic Ocean
Bibliographic record
Abstract
Abstract Elevated biological concentrations of methylmercury (MeHg), a bioaccumulative neurotoxin, are observed throughout the Arctic Ocean, but major sources and degradation pathways in seawater are not well understood. We develop a mass budget for mercury species in the Arctic Ocean based on available data since 2004 and discuss implications and uncertainties. Our calculations show that high total mercury (Hg) in Arctic seawater relative to other basins reflect large freshwater inputs and sea ice cover that inhibits losses through evasion. We find that most net MeHg production (20 Mg a −1 ) occurs in the subsurface ocean (20–200 m). There it is converted to dimethylmercury (Me 2 Hg: 17 Mg a −1 ), which diffuses to the polar mixed layer and evades to the atmosphere (14 Mg a −1 ). Me 2 Hg has a short atmospheric lifetime and rapidly degrades back to MeHg. We postulate that most evaded Me 2 Hg is redeposited as MeHg and that atmospheric deposition is the largest net MeHg source (8 Mg a −1 ) to the biologically productive surface ocean. MeHg concentrations in Arctic Ocean seawater are elevated compared to lower latitudes. Riverine MeHg inputs account for approximately 15% of inputs to the surface ocean (2.5 Mg a −1 ) but greater importance in the future is likely given increasing freshwater discharges and permafrost melt. This may offset potential declines driven by increasing evasion from ice‐free surface waters. Geochemical model simulations illustrate that for the most biologically relevant regions of the ocean, regulatory actions that decrease Hg inputs have the capacity to rapidly affect aquatic Hg concentrations.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".