Biogenic Aerosol in the Arctic from Eight Years of MSA Data from Ny Ålesund (Svalbard Islands) and Thule (Greenland)
Bibliographic record
Abstract
In remote marine areas, biogenic productivity and atmospheric particulate are coupled through dimethylsulfide (DMS) emission by phytoplankton. Once in the atmosphere, the gaseous DMS is oxidized to produce H2SO4 and methanesulfonic acid (MSA); both species can affect the formation of cloud condensation nuclei. This study analyses eight years of biogenic aerosol evolution and variability at two Arctic sites: Thule (76.5° N, 68.8° W) and Ny Ålesund (78.9° N, 11.9° E). Sea ice plays a key role in determining the MSA concentration in polar regions. At the beginning of the melting season, in April, up to June, the biogenic aerosol concentration appears inversely correlated with sea ice extent and area, and positively correlated with the extent of the ice-free area in the marginal ice zone (IF-MIZ). The upper ocean stratification induced by sea ice melting might have a role in these correlations, since the springtime formation of this surface layer regulates the accumulation of phytoplankton and nutrients, allowing the DMS to escape from the sea to the atmosphere. The multiyear analysis reveals a progressive decrease in MSA concentration in May at Thule and an increase in July August at Ny Ålesund. Therefore, while the MSA seasonal evolution is mainly related with the sea ice retreat in April, May, and June, the IF-MIZ extent appears as the main factor affecting the longer-term behavior of MSA.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".