Heterogeneous nucleation of ice in (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub>‐H<sub>2</sub>O particles with mineral dust immersions
Bibliographic record
Abstract
Using optical microscopy, we investigated the heterogeneous nucleation of ice in aqueous (NH4)2SO4‐H2O particles containing two types of mineral dusts, kaolinite and montmorillonite. The efficacy of montmorillonite and kaolinite to nucleate ice in (NH4)2SO4‐H2O particles is similar. The difference in freezing temperatures, compared to the homogeneous freezing temperatures, is found to vary from 8–20 K and it is larger for particles with concentrations greater than 27 wt %. Our freezing data shows that for temperatures ranging from 239 K to 198 K, ice super‐saturations between 1.35 and 1.51 are required for ice to heterogeneously nucleate in NH4SO4‐H2O particles containing mineral dust immersions. Based on our results, we conclude mineral dust is an efficient nuclei for ice in NH4SO4‐H2O aerosols and as a result, it can initiate the formation of upper tropospheric ice clouds at warmer temperatures and lower super‐saturations in comparison to homogeneous freezing.
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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".