What are coarse dust aerosols, and how do they impact the Earth's climate system?
Bibliographic record
Abstract
Mineral dust is an important aerosol specie in the atmosphere that impacts the Earth’s climate system through its interactions with radiation, clouds, hydrology, atmospheric chemistry, and biogeochemistry. Because dust sizes span more than three orders of magnitude in diameter and dust properties are size-dependent, most previous studies separate dust particles into different classes – broadly defined as fine and coarse dust – which could produce distinct impacts on the Earth system. However, there are general inconsistencies in the terminology, the diameter boundaries, and diameter ranges currently attributed to dust size classes across the literature. As part of a comprehensive review of coarse dust recently completed, we propose, with justification, a new uniform classification that defines coarse and super-coarse dust as particles between 2.5 - 10 µm and 10 - 62.5 µm in diameter, respectively. In addition, we will show several lines of observational evidence that indicate coarse and super-coarse dust particles are transported much farther than previously expected and that the abundance of these particles is substantially underestimated in current global models. Despite the limitations of representing coarse and super-coarse dust aerosols in models, we will highlight their unique impacts on several aspects of the Earth's climate system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".