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Record W2794301846 · doi:10.1002/2017gl076357

Oxidative Processing Lowers the Ice Nucleation Activity of Birch and Alder Pollen

2018· article· en· W2794301846 on OpenAlexafffund
Ellen Gute, Jonathan P. D. Abbatt

Bibliographic record

VenueGeophysical Research Letters · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsIce nucleusSupersaturationNucleationAlderCloud chamberPollenChemistryEnvironmental chemistryChemical engineeringOrganic chemistryBotanyBiology

Abstract

fetched live from OpenAlex

Abstract Pollen carry water extractable compounds with ice nucleating (IN) activity. This study investigates whether the hydroxyl radical, as the major atmospheric oxidant, can affect the IN activity of silver birch and grey alder subpollen particles under in‐cloud conditions for deposition freezing mode conditions at 234 K. It is found that oxidation increases the supersaturation ratio with respect to ice necessary for the onset of ice nucleation and decreases the fraction of particles which initiate ice nucleation. This reduction of IN activity under equivalent oxidation conditions does not occur with a mineral dust sample (Arizona Test Dust). Chemical analysis of fresh and oxidized pollen material indicates a change of molecular structure with a loss of conjugation and an increase in oxidized functional groups, such as carbonyls. This is the first demonstration that in‐cloud oxidation may lower the IN abilities of biological particles such as pollen.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 head0.033
GPT teacher head0.298
Teacher spread0.266 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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".

Quick stats

Citations27
Published2018
Admission routes2
Has abstractyes

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