Hygroscopic Seeding Effects of Giant Aerosol Particles Simulated by the Lagrangian‐Particle‐Based Direct Numerical Simulation
Bibliographic record
Abstract
Abstract This study investigated the microphysical responses to seeding giant aerosol particles and supersaturation fluctuations. A Lagrangian‐particle‐based direct numerical simulation is used to resolve the interactions among individual aerosols, droplets, and the fluctuating supersaturation field within a turbulent, adiabatic air parcel. It is shown that the giant seeding particles exert strong solute effects throughout the entire simulation to alter the subsequent collision–coalescence process, implying the importance of including the solute term in droplet growth. Small‐scale supersaturation fluctuations in adiabatic cloud regions have a negligible influence on aerosol activation and droplet condensation. This is because in regions free of entrainment and/or large‐scale mixing, the weak supersaturation fluctuations can be quickly smoothed out via diffusion and remain relatively small in magnitude (with a standard deviation <). In contrast, the activation in our simulations is determined by the seeding modulation of the parcel‐mean supersaturation.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".