A decade of dust: Asian dust and springtime aerosol load in the U.S. Pacific Northwest
Bibliographic record
Abstract
We integrate SeaWiFS aerosol optical thickness (AOT) over the Taklamakan and Gobi Deserts with U.S. aerosol observations to study surface aerosol variability in the Northwest U.S. in relation to Asian dust emissions. The results indicate that ∼50% of the interannual variability in springtime average PM2.5 and PM10 can be explained by changes in Asian dust emissions. On a seasonal timescale, variations in dust emissions appear to be more important in determining the total material crossing the Pacific than the variations in meteorology represented by the PNA or the LRT3 indices. We are able to explain ∼80% of the interannual variability using three variables: AOT, a transport index, and regional precipitation. This suggests that a strong source, favorable transport and sufficient residence time are needed for Asian dust to have a maximum seasonal impact in the Northwest. The results contextualize case studies and demonstrate the utility of the Deep Blue algorithm.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".