Upward transport of bottom-ice dimethyl sulfide during advanced melting of arctic first-year sea ice
Bibliographic record
Abstract
This paper presents the first empirical estimates of dimethyl sulfide (DMS) gas fluxes across permeable sea ice in the Arctic. DMS is known to act as a major potential source of aerosols that strongly influence the Earth’s radiative balance in remote marine regions during the ice-free season. Results from a sampling campaign, undertaken in 2015 between June 2 and June 28 in the ice-covered Western Baffin Bay, revealed the presence of high algal biomass in the bottom 0.1-m section of sea ice (21 to 380 µg Chl a L–1) combined with the presence of high DMS concentrations (212–840 nmol L–1). While ice algae acted as local sources of DMS in bottom sea ice, thermohaline changes within the brine network, from gravity drainage to vertical stabilization, exerted strong control on the distribution of DMS within the interior of the ice. We estimated both the mean DMS molecular diffusion coefficient in brine (5.2 × 10–5 cm2 s–1 ± 51% relative S.D., n = 10) and the mean bulk transport coefficient within sea ice (33 × 10–5 cm2 s–1 ± 41% relative S.D., n = 10). The estimated DMS fluxes ± S.D. from the bottom ice to the atmosphere ranged between 0.47 ± 0.08 µmol m–2 d–1 (n = 5, diffusion) and 0.40 ± 0.15 µmol m–2 d–1 (n = 5, bulk transport) during the vertically stable phase. These fluxes fall within the lower range of direct summer sea-to-air DMS fluxes reported in the Arctic. Our results indicate that upward transport of DMS, from the algal-rich bottom of first-year sea ice through the permeable sea ice, may represent an important pathway for this biogenic gas toward the atmosphere in ice-covered oceans in spring and summer.
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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 | 0.000 | 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) | 0.000 | 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".