MétaCan
Menu
Back to cohort
Record W1644074212 · doi:10.1029/2001gl014289

Heterogeneous nucleation of ice in (NH<sub>4</sub>)<sub>2</sub>SO<sub>4</sub>‐H<sub>2</sub>O particles with mineral dust immersions

2002· article· en· W1644074212 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueGeophysical Research Letters · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIce nucleusKaoliniteNucleationMontmorilloniteMineralMineralogyHomogeneousAqueous solutionMaterials scienceAnalytical Chemistry (journal)ChemistryThermodynamicsEnvironmental chemistryPhysicsPhysical chemistry

Abstract

fetched live from OpenAlex

Using optical microscopy, we investigated the heterogeneous nucleation of ice in aqueous (NH 4 ) 2 SO 4 ‐H 2 O particles containing two types of mineral dusts, kaolinite and montmorillonite. The efficacy of montmorillonite and kaolinite to nucleate ice in (NH 4 ) 2 SO 4 ‐H 2 O particles is similar. The difference in freezing temperatures, compared to the homogeneous freezing temperatures, is found to vary from 8–20 K and it is larger for particles with concentrations greater than 27 wt %. Our freezing data shows that for temperatures ranging from 239 K to 198 K, ice super‐saturations between 1.35 and 1.51 are required for ice to heterogeneously nucleate in NH 4 SO 4 ‐H 2 O particles containing mineral dust immersions. Based on our results, we conclude mineral dust is an efficient nuclei for ice in NH 4 SO 4 ‐H 2 O aerosols and as a result, it can initiate the formation of upper tropospheric ice clouds at warmer temperatures and lower super‐saturations in comparison to homogeneous freezing.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.038
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.024
GPT teacher head0.237
Teacher spread0.213 · 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