MétaCan
Menu
Back to cohort
Record W2055852551 · doi:10.5194/acp-8-545-2008

Surface observation of sand and dust storm in East Asia and its application in CUACE/Dust

2008· article· en· W2055852551 on OpenAlexaff
Y. Q. Wang, X. Y. Zhang, S. L. Gong, Chunhong Zhou, Xuegang Hu, H. L. Liu, Tao Niu, Y. Q. Yang

Bibliographic record

VenueAtmospheric chemistry and physics · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Key Research and Development Program of China
KeywordsDust stormChinaAsian DustEnvironmental scienceEast AsiaClimatologyInner mongoliaVisibilityStormGeographyMeteorologyPhysical geographyAerosolAtmospheric sciencesGeology

Abstract

fetched live from OpenAlex

Abstract. The spatial-temporal distributions and sources of sand and dust storm (SDS) in East Asia from 2001 to 2006 were investigated on the basis of visibility and PM10 data from the routine SDS and weather monitoring networks run by CMA (China Meteorological Administration). A power functional relationships between PM10 and visibility was found among various regions generally with a good correlation (r2=0.90), especially in Asian SDS source regions. In addition, three SDS occurrence centers, i.e. western China, Mongolia and northern China, were identified with the Mongolia source contributing more dust to the downwind areas including Korea and Japan than other two sources. Generally, high PM10 concentrations were observed in most areas of northern China. The highest value was obtained in the center of western China with a spring daily mean value of 876 μgm−3, and the value in other source regions exceeds 200 μgm−3. These data sets together with the satellite observations in China form the main observation database for the evaluation and data assimilation of CUACE/Dust system – an operational SDS forecasting system for East Asia.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.626

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 studies0.0000.000
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 head0.016
GPT teacher head0.202
Teacher spread0.185 · 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

Citations121
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueAtmospheric chemistry and physicsSame topicAtmospheric chemistry and aerosolsFrench-language works237,207