The immersion freezing behavior of mineral dust particles mixed with biological substances
Bibliographic record
Abstract
Abstract. Biological particles such as bacteria, fungal spores or pollen are known to be efficient ice nucleating particles. Their ability to nucleate ice is due to ice nucleation active macromolecules (INM). It has been suggested that these INM maintain their nucleating ability even when they are separated from their original carriers. This opens the possibility of an accumulation of such INM in e.g., soils, resulting in an internal mixture of mineral dust and INM. If particles from such soils which contain biological INM are then dispersed into the atmosphere due to wind erosion or agricultural processes, they could induce ice nucleation at temperatures typical for biological substances, i.e., above −20 up to almost 0 °C. To explore this hypothesis, we performed a measurement campaign within the research unit INUIT, where we investigated the ice nucleation behavior of mineral dust particles internally mixed with INM. Specifically, we mixed a pure mineral dust sample (illite-NX) with ice active biological material (birch pollen washing water) and quantified the immersion freezing behavior of the resulting particles utilizing the Leipzig Aerosol Cloud Interaction Simulator (LACIS). To characterize the mixing state of the generated aerosol we used different methods which will also be discussed. We found that internally mixed particles, containing ice active biological material, follow the ice nucleation behavior observed for the purely biological particles, i.e. freezing occurs at temperatures at which mineral dusts themselves are not yet ice active. It can be concluded that INM located on a mineral dust particle determine the freezing behavior of that particle.
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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) | 0.001 | 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".