Properties of Seawater Surfactants Associated with Primary Marine Aerosol Particles Produced by Bursting Bubbles at a Model Air–Sea Interface
Bibliographic record
Abstract
Surfactants account for minor fractions of total organic carbon in the ocean but can significantly influence the production of primary marine aerosol particles (PMA) at the sea surface via modulation of bubble surface tension. During September and October 2016, model PMA (mPMA) were produced from seawater by bursting bubbles at two biologically productive and two oligotrophic stations in the western North Atlantic Ocean. Total concentrations of surfactants extracted from mPMA and seawater were quantified and characterized via measurements of surface tension isotherms and critical micelle concentrations (CMCs). Surfactant CMCs in biologically productive seawater were lower than those in the oligotrophic seawater suggesting that surfactant mixtures in the two regions were chemically distinct. mPMA surfactants were enriched in all regions relative to those in the associated seawater. Surface tension isotherms indicate that mPMA surfactants were weaker than corresponding seawater surfactants. mPMA from biologically productive seawater contained higher concentrations of surfactants than those produced from oligotrophic seawater, supporting the hypothesis that seawater surfactant properties modulate mPMA surfactant concentrations. Diel variability in concentrations of seawater and mPMA surfactants in some regions is consistent with biological and/or photochemical processing. This work demonstrates direct links between surfactants in mPMA and those in the associated seawater.
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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 | 0.000 | 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".