Aggregation of ice-nucleating macromolecules from <i>Betula pendula</i> pollen determines ice nucleation efficiency
Bibliographic record
Abstract
Abstract. Various aerosols, including mineral dust, soot, and biological particles, can act as ice nuclei, initiating the freezing of supercooled cloud droplets. Cloud droplet freezing significantly impacts cloud properties and, consequently, weather and climate. Some biological ice nuclei exhibit exceptionally high nucleation temperatures close to 0 °C. Ice-nucleating macromolecules (INMs) found on pollen are typically not considered among the most active ice nuclei. Still, they can be highly abundant, especially for species such as Betula pendula, a widespread birch tree species in the boreal forest. Recent studies have shown that certain tree-derived INMs exhibit ice nucleation activity above −10 °C, suggesting they could play a more significant role in atmospheric processes than previously understood. Our study reveals that three distinct INM classes active at −8.7, −15.7, and −17.4 °C are present in B. pendula. Freeze drying and freeze–thaw cycles noticeably alter their ice nucleation capability, and the results of heat treatment, size, and chemical analysis indicate that INM classes correspond to size-varying aggregates, with larger aggregates nucleating ice at higher temperatures, in agreement with previous studies on fungal and bacterial ice nucleators. Our findings suggest that B. pendula INMs are potentially important for atmospheric ice nucleation because of their high prevalence and nucleation temperatures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".