Ice nucleation properties of rust and bunt fungal spores and their transport to high altitudes, where they can cause heterogeneous freezing
Bibliographic record
Abstract
Rust and bunt spores that act as ice nuclei (IN) could change the formation characteristics and properties of ice‐containing clouds. In addition, ice nucleation on rust and bunt spores, followed by precipitation, may be an important removal mechanism of these spores from the atmosphere. Using an optical microscope, we studied the ice nucleation properties of spores from four rust species (Puccinia graminis, Puccinia triticina, Puccinia allii, and Endocronartium harknesssii) and two bunt species (Tilletia laevis and Tilletia tritici) immersed in water droplets. We show that the cumulative number of IN per spore is 5 × 10−3, 0.01, and 0.10 at temperatures of roughly −24°C, −25°C, and −28°C, respectively. Using a particle dispersion model, we also investigated if these rust and bunt spores will reach high altitudes in the atmosphere where they can cause heterogeneous freezing. Simulations suggest that after 3 days and during periods of high spore production, between 6 and 9% of 15 µm particles released over agricultural regions in Kansas (U.S.), North Dakota (U.S.), Saskatchewan (Canada), and Manitoba (Canada) can reach at least 6 km in altitude. An altitude of 6 km corresponds to a temperature of roughly −25°C for the sites chosen. The combined results suggest that (a) ice nucleation by these fungal spores could play a role in the removal of these particles from the atmosphere and (b) ice nucleation by these rust and bunt spores are unlikely to compete with mineral dust on a global and annual scale at an altitude of approximately 6 km.
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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".