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Record W1890793174 · doi:10.1029/2008gl035997

Effects of sulfuric acid and ammonium sulfate coatings on the ice nucleation properties of kaolinite particles

2009· article· en· W1890793174 on OpenAlexaff
Michael L. Eastwood, Sébastien Cremel, Michael Wheeler, Benjamin J. Murray, Éric Girard, Allan K. Bertram

Bibliographic record

VenueGeophysical Research Letters · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversité de MontréalUniversité du Québec à MontréalUniversity of British Columbia
FundersNatural Environment Research CouncilSight Research UK
KeywordsIce nucleusEfflorescenceKaoliniteNucleationRelative humiditySulfuric acidMaterials scienceAmmonium sulfateMineralogyClear iceAmmoniumChemical engineeringGeologyChemistryArctic ice packMeteorologyClimatologyMetallurgyAntarctic sea iceSea icePhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

The onset conditions for ice nucleation on H 2 SO 4 coated, (NH 4 ) 2 SO 4 coated, and uncoated kaolinite particles at temperatures ranging from 233 to 246 K were studied. We define the onset conditions as the relative humidity and temperature at which the first ice nucleation event was observed. Uncoated particles were excellent ice nuclei; the onset relative humidity with respect to ice (RH i ) was below 110% at all temperatures studied, consistent with previous measurements. H 2 SO 4 coatings, however, drastically altered the ice nucleating ability of kaolinite particles, increasing the RH i required for ice nucleation by approximately 30%, similar to the recent measurements by Möhler et al. [2008b]. (NH 4 ) 2 SO 4 coated particles were poor ice nuclei at 245 K, but effective ice nuclei at 236 K. The differences between H 2 SO 4 and (NH 4 ) 2 SO 4 coatings may be explained by the deliquescence and efflorescence properties of (NH 4 ) 2 SO 4 . These results support the idea that emissions of SO 2 and NH 3 may influence the ice nucleating properties of mineral dust particles.

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 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 score0.134
Threshold uncertainty score0.188

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)0.0000.000

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 head0.027
GPT teacher head0.245
Teacher spread0.218 · 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.

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

Citations131
Published2009
Admission routes1
Has abstractyes

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