Bioaerosols and atmospheric ice nuclei in a Mediterranean dryland: community changes related to rainfall
Bibliographic record
Abstract
Abstract. Certain biological particles are highly efficient ice nuclei (IN), but the actual contribution of bioparticles to the pool of atmospheric IN and their relation to precipitation are not well characterized. We investigated the composition of bioaerosols, ice nucleation activity, and the effect of rainfall by metagenomic sequencing and freezing experiments of aerosol samples collected during the INUIT 2016 campaign in a rural dryland on the eastern Mediterranean island of Cyprus. Taxonomic analysis showed community changes related to rainfall. For the rain-affected samples, we found higher read proportions of fungi, particularly of Agaricomycetes, which are a class of fungi that actively discharge their spores into the atmosphere in response to humidity changes. In contrast, the read proportions of bacteria were reduced, indicating an effective removal of bacteria by precipitation. Freezing experiments showed that the IN population in the investigated samples was influenced by both rainfall and dust events. For example, filtration and heat treatment of the samples collected during and immediately after rainfall yielded enhanced fractions of heat-sensitive IN in the size ranges larger than 5 µm and smaller than 0.1 µm, which were likely of biological origin (entire bioparticles and soluble macromolecular bio-IN). In contrast, samples collected in periods with dust events were dominated by heat-resistant IN active at lower temperatures, most likely mineral dust. The DNA analysis revealed low numbers of reads related to microorganisms that are known to be IN-active. This may reflect unknown sources of atmospheric bio-IN as well as the presence of cell-free IN macromolecules that do not contain DNA, in particular for sizes < 0.1 µm. The observed effects of rainfall on the composition of atmospheric bioaerosols and IN may influence the hydrological cycle (bioprecipitation cycle) as well as the health effects of air particulate matter (pathogens, allergens).
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".