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Record W2016961897 · doi:10.1029/2009gl039429

On the availability of uncoated mineral dust ice nuclei in cold cloud regions

2009· article· en· W2016961897 on OpenAlexaff
Aldona Wiacek, Thomas Peter

Bibliographic record

VenueGeophysical Research Letters · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsDalhousie University
Fundersnot available
KeywordsIce nucleusAtmospheric sciencesEnvironmental scienceMineral dustRelative humidityClear iceSaturation (graph theory)Liquid waterNucleationSea ice growth processesMineralLiquid water contentHomogeneousIce cloudGeologyArctic ice packAerosolMeteorologyMaterials scienceClimatologyCloud computingSea ice thicknessAntarctic sea icePhysicsEarth scienceSea iceThermodynamics

Abstract

fetched live from OpenAlex

The ice nucleation efficiency of mineral dust decreases when it acquires coatings, e.g. through processing in liquid clouds. This study explores the availability of unprocessed mineral dust for interactions with clouds. We performed forward trajectory calculations originating near the surface of the Chinese Taklimakan desert. The initial specific humidity of each trajectory was assumed to be conserved and used to calculate the relative humidities with respect to water and ice, allowing us to estimate the formation of liquid, mixed‐phase and ice clouds downstream. Practically none of the simulated air parcels reached conditions suitable for homogeneous nucleation of ice ( T ≲ −40°C) without experiencing water saturation first. Potentially the biggest impact of mineral dust is predicted to be on mixed‐phase clouds. Furthermore, most trajectories passed through ice‐saturated (but water‐subsaturated) regions where “warm” ( T ≳ −40°C) ice clouds may form prior to mixed‐phase clouds.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.705

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.029
GPT teacher head0.282
Teacher spread0.254 · 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 designObservational
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

Citations34
Published2009
Admission routes1
Has abstractyes

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