Temporal Evolution of Gaseous Mercury Across the Sea Ice‐Seawater Interface: A Mesocosm Study
Bibliographic record
Abstract
Abstract In the marine cryosphere, seasonal sea ice dynamics affect the behavior of gaseous mercury, yet the mechanism remains poorly understood. By carrying out an outdoor sea ice mesocosm study, we examine primarily the abiotic factors influencing mercury dynamics and show distinct behaviors of gaseous mercury across the sea ice‐seawater interface over the full growth‐melt cycle. The distribution of gaseous mercury in sea ice is influenced by entrapment of gaseous mercury from different sources into sea ice of different textures, transport schemes within sea ice, and in situ cryo‐processes that affect mercury speciation. In the growing sea ice sections where solar radiation penetrated, production of gaseous mercury was observed, supporting the occurrence of in‐ice cryo‐photoreduction of divalent mercury. In under‐ice seawater, concentrations of dissolved gaseous mercury decreased gradually during ice growth and increased rapidly to pre‐freezing levels as ice started to melt, suggesting that the atmosphere‐sea ice‐seawater exchange pathway of gaseous mercury could be re‐established through melting first‐year sea ice. Our results from this unique mesocosm study provide new insights on the dynamics of gaseous mercury in and around sea ice that are primarily driven by abiotic processes, assisting model parameterizations for mercury cycling in polar regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".