Causes and Effects of Sand and Dust Storms: What Has Past Research Taught Us? A Survey
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
Barren ground and sites with low coverage by vegetation (e.g., dunes, soil surfaces, dry lakes, and riverbeds) are the main source areas of sand and dust storms (SDS). The understanding of causes, processes (abrasion, deflation, transport, deposition), and influencing factors of sandy and dusty particles moving by wind both in the boundary layer and in the atmosphere are basic prerequisites to distinguish between SDS. Dust transport in the atmosphere modulates radiation, ocean surface temperature, climate, as well as snow and ice cover. The effects of airborne particles on land are varied and can cause advantages and disadvantages, both in source areas and in sink or deposition areas, with disturbances of natural environments and anthropogenic infrastructure. Particulate matter in general and SDS specifically can cause severe health problems in human respiratory and other organs, especially in children. Economic impacts can be equally devastating, but the costs related to SDS are not thoroughly studied. The available data show huge economic damages caused by SDS and by the mitigation of their effects. Management of SDS-related hazards utilizes remote sensing techniques, on-site observations, and protective measures. Integrated strategies are necessary during both the planning and monitoring of these measures. Such integrated strategies can be successful when they are developed and implemented in close cooperation with the local and regional population and stakeholders.
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.
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 | 0.001 | 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