Marine Aerosol Production via Detrainment of Bubble Plumes Generated in Natural Seawater With a Forced‐Air Venturi
Bibliographic record
Abstract
Abstract During September–October 2016, a marine aerosol generator configured with forced‐air Venturis was deployed at two biologically productive and two oligotrophic regions of the western North Atlantic Ocean to investigate factors that modulate primary marine aerosol (PMA) production. The generator produced representative bubble size distributions with Hinze scales (0.32 to 0.95 mm radii) and void fractions (0.011 to 0.019 Lair Lsw‐1) that overlapped those of plumes produced in the surface ocean by breaking wind waves. Hinze scales and void fractions of bubble plumes varied among seawater hydrographic regions, whereas corresponding peaks and widths of bubble size distributions did not, suggesting that variability in seawater surfactants drove variability in plume dynamics. Peaks in size‐resolved number production efficiencies for model PMA (mPMA) emitted via bubble bursting in the generator were within a narrow range (0.059 to 0.069 μm geometric mean diameter) over wide ranges in subsurface bubble characteristics, suggesting that subsurface bubble size distributions were not the primary controlling factors as was suggested by previous work. Total mass production efficiencies for mPMA decreased with increasing air detrainment rates, supporting the hypothesis that surface bubble rafts attenuate mPMA mass production. Total mass and Na+ production efficiencies for mPMA from biologically productive seawater were significantly greater than those from oligotrophic seawater. Corresponding mPMA number distributions peaked at smaller sizes during daytime, suggesting that short‐lived surfactants of biological and/or photochemical origin modulated diel variability in marine aerosol production.
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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".