Exploring the role of aggregation in ice-nucleating macromolecules of Betula pendula pollen
Bibliographic record
Abstract
A wide range of aerosols, including dust, soot, and biological particles, can serve as ice nuclei, initiating the freezing of supercooled cloud droplets. This process significantly impacts cloud characteristics, and consequently, weather and climate. Among biological ice nuclei, some exhibit exceptionally high nucleation temperatures. While Ice Nucleating Macromolecules (INMs) found on pollen are typically not among the most active ice nuclei, they are abundant, as evidenced by their presence throughout the tissues of trees. Notably, recent studies have shown that certain tree-based INMs, such as those from Betula pendula, demonstrate ice nucleation activity above -10°C. These findings suggest that INMs emitted from the biosphere could play a more significant role in atmospheric processes than previously understood.Our research delves into the properties of Betula pendula INMs through comprehensive ice-nucleation assays. We explore the stability of these INMs and the factors influencing their ice nucleation activity. Our approach integrates experimental data with size measurements and chemical analyses to better comprehend the underlying mechanisms.Our findings reveal that Betula pendula INMs comprise three distinct classes active at -6°C, -15°C, and -18°C, each present in varying concentrations. We observed that freeze-drying and freeze-thaw cycles markedly alter their ice nucleation capacity. Additionally, heat treatments and chemical analysis suggest that these INM classes may be size-varying aggregates, with larger aggregates being more efficient at nucleating ice. This hypothesis aligns with previous studies on fungal and bacterial ice nucleators. Our research highlights the significance of birch INMs in atmospheric ice nucleation, not only because of their prevalence but also due to their occasional but notable high nucleation temperatures.
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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".