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Record W2096635607 · doi:10.1029/2005gl025397

Aerosol indirect effect over the Indian Ocean

2006· article· en· W2096635607 on OpenAlexaff
Petr Chýlek, Manvendra K. Dubey, Ulrike Lohmann, V. Ramanathan, Yoram J. Kaufman, Glen Lesins, James G. Hudson, G. Altmann, S. C. Olsen

Bibliographic record

VenueGeophysical Research Letters · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAerosolEnvironmental scienceAtmospheric sciencesEffective radiusModerate-resolution imaging spectroradiometerClimatologyIce nucleusNucleationIce crystalsPollutionSatelliteMeteorologyGeologyPhysics

Abstract

fetched live from OpenAlex

We analyze the MODIS (Moderate Resolution Imaging Spectroradiometer) satellite data over the seas adjacent to the Indian sub‐continent to investigate the effect of aerosols on the size distribution of cloud droplets and ice crystals (indirect aerosol effect). During the winter months of increased anthropogenic pollution we observe smaller sizes of cloud droplets in water clouds in agreement with the expected aerosol indirect effect. However, contrary to our expectations, we find that during episodes of increased pollution the effective radius of ice crystals is shifted toward the larger rather than smaller sizes. We propose a combination of natural seasonal variability of meteorological conditions and an “inverse aerosol indirect effect” caused by heterogeneous ice nucleation as a possible explanation of observed ice crystal growth. The ECHAM4 (European Center for Medium‐Range Weather Forecast Hamburg version 4th generation GCM) results with heterogeneous ice nucleation reproduce the observed increase in ice crystal size during the enhanced pollution episodes.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.010
GPT teacher head0.264
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; both teacher heads agree on what is shown here.

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

Citations86
Published2006
Admission routes1
Has abstractyes

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