Modeling the Mercury Cycle in the Sea Ice Environment: A Buffer between the Polar Atmosphere and Ocean
Bibliographic record
Abstract
Sea ice (including overlying snow) is a dynamic interface between the atmosphere and the ocean, influencing the mercury (Hg) cycling in polar oceans. However, a large-scale and process-based model for the Hg cycle in the sea ice environment is lacking, hampering our understanding of regional Hg budget and critical processes. Here, we develop a comprehensive model for the Hg cycle at the ocean-sea ice-atmosphere interface with constraints from observational polar cryospheric data. We find that seasonal patterns of average total Hg (THg) in snow are governed by snow thermodynamics and deposition, peaking in springtime (Arctic: 5.9 ng/L; Antarctic: 5.3 ng/L) and minimizing during ice formation (Arctic: 1.0 ng/L, Antarctic: 0.5 ng/L). Arctic and Antarctic sea ice exhibited THg concentration peaks in summer (0.25 ng/L) and spring (0.28 ng/L), respectively, governed by different snow Hg transmission pathways. Antarctic snow-ice formation facilitates Hg transfer to sea ice during spring, while in the Arctic, snow Hg is primarily moved through snowmelt. Overall, first-year sea ice acts as a buffer, receiving atmospheric Hg during ice growth and releasing it to the ocean in summer, influencing polar atmospheric and seawater Hg concentrations. Our model can assess climate change effects on polar Hg cycles and evaluate the Minamata Convention's effectiveness for Arctic populations.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".