How efficient is cloud droplet formation of organic aerosols?
Bibliographic record
Abstract
Based on laboratory findings that small amounts of a soluble aerosol, such as ammonium sulfate (AS), drastically decrease the activation diameter of moderately soluble organic aerosols, we performed studies with an adiabatic parcel model for cloud droplet nucleation. Moderately soluble organics, such as adipic acid (AA), which represents a class of partially soluble aerosols as found in atmospheric aerosols, require a larger supersaturation and result in fewer cloud droplets as compared to pure AS and vice versa when compared to a completely insoluble species such as dust. Adding only 10% AS to AA dramatically increases its ability to become activated resulting in 36–92% of the cloud droplets that would be obtained from pure AS, whereas the droplet concentration in the almost pure AA aerosol is only 11–47% of that of AS. Addition of a surface active species, such as nonanoic acid, instead of AS to AA reduces its activated fraction by 3–34% as compared to the AA/AS system.
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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 | 0.001 |
| 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 | 0.001 |
| 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".