Bibliographic record
Abstract
A wide range of materials including mineral dust, soil dust, and bioaerosols have been shown to act as ice nuclei in the atmosphere. During atmospheric transport, these materials can become coated with inorganic and organic solutes which may impact their ability to nucleate ice. While a number of studies have investigated the impact of solutes at low concentrations on ice nucleation by mineral dusts, very few studies have examined their impact on non-mineral dust ice nuclei. We studied the effect of dilute (NH 4 ) 2 SO 4 solutions (0.05 M) on immersion freezing of a variety of non-mineral dust ice-nucleating substances (INSs) including bacteria, fungi, sea ice diatom exudates, sea surface microlayer substances, and humic substances using the droplet-freezing technique. We also studied the effect of (NH 4 ) 2 SO 4 solutions (0.05 M) on the immersion freezing of several types of mineral dust particles for comparison purposes. (NH 4 ) 2 SO 4 had no effect on the median freezing temperature ( Δ T 50 ) of 9 of the 10 non-mineral dust materials tested. There was a small but statistically significant decrease in Δ T 50 ( − 0.43 ± 0.19 ∘ C) for the bacteria Xanthomonas campestris in the presence of (NH 4 ) 2 SO 4 compared to pure water. Conversely, (NH 4 ) 2 SO 4 increased the median freezing temperature of four different mineral dusts (potassium-rich feldspar, Arizona Test Dust, kaolinite, montmorillonite) by 3 to 9 ∘ C and increased the ice nucleation active site density per gram of material ( n m ( T )) by a factor of ∼ 10 to ∼ 30. This significant difference in the response of mineral dust and non-mineral dust ice-nucleating substances when exposed to (NH 4 ) 2 SO 4 suggests that they nucleate ice and/or interact with (NH 4 ) 2 SO 4 via different mechanisms. This difference suggests that the relative importance of mineral dust to non-mineral dust particles for ice nucleation in mixed-phase clouds could potentially increase as these particles become coated with (NH 4 ) 2 SO 4 in the atmosphere. This difference also suggests that the addition of (NH 4 ) 2 SO 4 (0.05 M) to atmospheric samples of unknown composition could potentially be used as an indicator or assay for the presence of mineral dust ice nuclei, although additional studies are still needed as a function of INS concentration to confirm the same trends are observed for different INS concentrations than those used here. A comparison with results in the literature does suggest that our results may be applicable to a range of mineral dust and non-mineral dust INS concentrations.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.027 | 0.015 |
| Insufficient payload (model declined to judge) | 0.336 | 0.296 |
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".