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Record W1903891751 · doi:10.1002/2014gl060213

Laboratory chamber measurements of the longwave extinction spectra and complex refractive indices of African and Asian mineral dusts

2014· article· en· W1903891751 on OpenAlexafffund
Claudia Di Biagio, Paola Formenti, Sarah A. Styler, Edouard Pangui, Jean‐François Doussin

Bibliographic record

VenueGeophysical Research Letters · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoCentre National d’Etudes Spatiales
KeywordsLongwaveMineral dustExtinction (optical mineralogy)Particle-size distributionEnvironmental scienceAtmospheric sciencesRadiative transferAsian DustParticle (ecology)Particle sizeRange (aeronautics)AerosolMineralogyMaterials scienceChemistryGeologyPhysicsOpticsMeteorology

Abstract

fetched live from OpenAlex

Abstract In this study we present the first results from laboratory chamber experiments newly designed to investigate the longwave optical properties of mineral dust. Extinction spectra in the 2–16 µm range have been measured in situ ( T = 293 K, RH < 2%) for polydispersed pure dust aerosols generated from natural parent soils from Tunisia, Niger, and the Gobi desert. Data are used in combination with particle size distributions to estimate the complex refractive index of each dust sample. Our results show that the magnitude and spectral dependence of the dust extinction and refractive indices differ according to particle mineralogy, suggesting the necessity for regionally resolved optical properties for modeling dust radiative effects in the longwave. The magnitude of extinction is controlled by the particle size distribution and remains significant down to low coarse particle concentrations, indicating that the longwave effect of mineral dust persists throughout long‐range transport and is thus relevant at the global scale.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score0.398

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 studies0.0000.001
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 head0.032
GPT teacher head0.280
Teacher spread0.248 · 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

Citations44
Published2014
Admission routes2
Has abstractyes

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