Modeling study on distributions and variations of global dust aerosol sources and sinks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Based on a 10-year(1995~2004) simulation of dust emissions and dry and wet depositions with the global air quality model system GEM-AQ/EC,the global spatial and temporal variations of the dust aerosol sources and sinks were characterized.Global dust emissions are centered over the major desert regions where the North African deserts are estimated with the largest emission contribution to the global dust aerosol up to 66.6%;the high dust aerosol depositions are concentrated over the desert sources and their immediately downwind areas.Thereby,the net dust aerosol sinks are largely distributed around the desert regions forming a receptor zone with the net sinks of greater than 10t/(km2·a)between 0°N and 60°N from North Africa,Eurasia,west Pacific Ocean,the north Indian Ocean, North America to the Atlantic ocean.In five major deserts of North Africa,Arabian Peninsula,Central Asia,East Asia and Australia,the dust emissions and depositions present the significant seasonal variations,The regional depositions expecting Central Asia experience almost the same seasonal cycle with the emissions;both dust aerosol emissions and depositions oscillate seasonally with the largest amplitudes in East Asia and with the lowest amplitudes in the North Africa.The seasonal dust emissions and depositions peak in summer over Central Asia and the Arabian Peninsula as well as during spring in the other three regions.Over the 10 years,the global annual emission is averaged with(150094)Mt in a slightly rising trend.The inter-annual variability rate of dust emissions in North Africa is lowest(6.3%),up to 28.3% in East Asia and highest in Australia(45.0%).The dust aerosol depositions over global land decrease at a rate of around 9.9Mt/a,while they increase year to year over the oceans.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it