Summer sea-ice melting enhances phytoplankton and dimethyl sulfide (DMS) productions in the Weddell–Scotia Confluence
Bibliographic record
Abstract
The relationships among sea ice melting, phytoplankton assemblages, and the production of climate-relevant trace gases in the Southern Ocean are gaining increasing attention from the scientific community. This is especially true for dimethyl sulfide (DMS), which plays an important role in atmospheric chemistry by influencing the formation of sulfated aerosols with radiative impacts and constituting cloud condensation nuclei. In the current study, DMS and its precursors, dimethylsulfoniopropionate (DMSP) and chlorophyll a (Chl a), were quantified in the Weddell–Scotia Confluence (WSC) during the 2018 record ice extent minimum period. Mixed layer changes were found to be generally associated with spatial variation in sea ice melt, with the depth being six times deeper in ice-free, well-mixed regions than in seasonal ice-melting zones. The surface Chl a concentration increased from ice-free to ice-melting regions with elevated sea ice meltwater percentages and drawdown surface nutrient concentrations. The concentrations of surface and depth-integrated Chl a in the upper 150 m reached maxima in the ice-melting region with the highest fraction of sea ice meltwater, illustrating that sea ice melting promoted the occurrence of phytoplankton blooms. The DMS and DMSP concentrations in the vicinity of the ice-melting zone were approximately three times higher than those in the ice-free waters. The observations of this study show that the regions of ice melting in the WSC were a zone of particularly high sea–air fluxes of DMS because sea ice melting processes enhanced the production of biogenic sulfur, which could significantly contribute to the atmospheric budget of DMS in the polar regions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".