The Surface of Tree Tissues as Source of Extractable Ice Nucleating Macromolecules during Rainfall Events
Bibliographic record
Abstract
<p>Several biological particles are able to trigger heterogeneous ice nucleation at subzero temperatures above -38°C. Many plants species such as winter rye [1], certain berries [2], pines and birches [3, 4] are known to contain biological ice-nucleating particles (BINPs) or rather ice-nucleating macromolecules (INMs). However, the influence of these BINPs on atmospheric processes including cloud glaciation and precipitation formation, as well as transport mechanisms of BINPs from the land surface into the atmosphere remain uncertain. If those INMs are easily available on the surfaces of a plant, they could be washed down by heavy rain events and could add an important new source for BINPs in the atmosphere, which has not received enough attention in the past.</p><p>In this study, we have focused on alpine trees, which form INMs extractable from their surfaces. We examined ice nucleation activity of samples from different birches (Betula pendula) and pines (Pinus sylvestris) growing in the Alps in Austria, Europe. Filtered aqueous extracts of leaves, needles, bark and wood were analyzed in the laboratory in terms of heterogeneous ice nucleation using VODCA (Vienna Optical Droplet Crystallization Analyzer), a cryo-microscope  for  emulsion  samples.  All plant tissues contained INMs in the submicron size range. Furthermore, we conducted a field experiment, in which we investigated the possibility of INMs to be released from the surface of the trees into the atmosphere during rain showers.</p><p>[1] Brush, R.A., M. Griffith, and A. Mlynarz, Characterization and Quantification of Intrinsic Ice Nucleators in Winter Rye (Secale cereale) Leaves. Plant Physiol, 1994. <strong>104</strong>(2): p. 725-735.</p><p>[2] Felgitsch, L., et al., Heterogeneous Freezing of Liquid Suspensions Including Juices and Extracts from Berries and Leaves from Perennial Plants. Atmosphere, 2019. <strong>10</strong>(1): p. 1-22.</p><p> [3] Pomeroy, M.K., D. Siminovitch, and F. Wightman, Seasonal biochemical changes in the living bark and needles of red pine (Pinus resinosa) in relation to adaptation to freezing. Canadian Journal of Botany, 1970. <strong>48</strong>(5): p. 953-967.</p><p>[4] Felgitsch, L., et al., Birch leaves and branches as a source of ice-nucleating macromolecules. Atmospheric Chemistry and Physics, 2018. <strong>18</strong>(21): p. 16063-16079.</p><p>[5] Pummer, B.G., et al., Ice nucleation by water-soluble macromolecules. Atmospheric Chemistry and Physics, 2015. <strong>15</strong>(8): p. 4077-4091.</p>
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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 | 0.000 |
| 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 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".