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The Surface of Tree Tissues as Source of Extractable Ice Nucleating Macromolecules during Rainfall Events

2020· article· en· W3092777013 on OpenAlexaboutno aff
Hinrich Grothe, Teresa M. Seifried, Paul Bieber, Laura Felgitsch

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicTurfgrass Adaptation and Management
Canadian institutionsnot available
Fundersnot available
KeywordsIce nucleusNucleationPrecipitationChemistryAtmosphere (unit)Frost (temperature)BiophysicsBotanyBiologyMaterials scienceOrganic chemistryMeteorology

Abstract

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<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>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.218
Teacher spread0.208 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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