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Record W1992796439 · doi:10.1029/2003gl018206

Sources of Asian dust and role of climate change versus desertification in Asian dust emission

2003· article· en· W1992796439 on OpenAlexfundno aff
X. Y. Zhang, S. L. Gong, T. L. Zhao, R. Arimoto, Y. Q. Wang, Zhiyao Zhou

Bibliographic record

VenueGeophysical Research Letters · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAeolian processes and effects
Canadian institutionsnot available
FundersCanadian Foundation for Climate and Atmospheric Sciences
KeywordsDesertificationAsian DustDust stormEnvironmental scienceStormChinaPrecipitationAtmospheric dustClimate changeAtmospheric sciencesClimatologyPhysical geographyGeographyAerosolMeteorologyGeologyOceanography

Abstract

fetched live from OpenAlex

Simulations of Asian dust emissions over the past 43 years are presented based on a size‐dependent soil dust emission and transport model (NARCM) along with supporting data from a network of surface stations. The deserts in Mongolia and in western and northern China (mainly the Taklimakan and Badain Juran, respectively) contribute ∼70% of the total dust emissions; non‐Chinese sources account for ∼40% of this. Several areas, especially the Onqin Daga sandy land, Horqin sandy land, and Mu Us Desert, have increased in dust emissions over the past 20 years, but efforts to reduce desertification in these areas may have little effect on Asian dust emission amount because these are not key sources. The model simulations indicate that meteorology and climate have had a greater influence on the Asian dust emissions and associated Asian dust storm occurrences than desertification.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.052
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.042
GPT teacher head0.293
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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

Citations564
Published2003
Admission routes1
Has abstractyes

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