The ice nucleation ability of one of the most abundant types of fungal spores found in the atmosphere
Bibliographic record
Abstract
Abstract. Recent atmospheric measurements show that biological particles are a potentially important class of ice nuclei. Types of biological particles that may be good ice nuclei include bacteria, pollen and fungal spores. We studied the ice nucleation properties of water droplets containing fungal spores from the genus Cladosporium, one of the most abundant types of spores found in the atmosphere. For water droplets containing a Cladosporium spore surface area of ~217 μm2 (equivalent to ~5 spores with average diameters of 3.2 μm ), 1% of the droplets froze by −28.5 °C and 10% froze by –30.1 °C. However, there was a strong dependence on freezing temperature with the spore surface area of Cladosporium within a given droplet. Mean freezing temperatures for droplets containing 1–5 spores are expected to be approximately −35.1 ± 2.3 °C (1σ S. D.). Atmospheric ice nucleation on spores of Cladosporium sp., or other spores with similar surface properties, thus do not appear to explain recent atmospheric measurements showing that biological particles participate as atmospheric ice nuclei. The poor ice nucleation ability of Cladosporium sp. may be attributed to the surface which is coated with hydrophobins (a class of hydrophobic proteins that appear to be widespread in filamentous fungi). Given the ubiquity of hydrophobins on spore surfaces, the current study may be applicable to many fungal species of atmospheric importance.
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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".