Climatological‐Scale Analysis of Intensive and Semi‐intensive Aerosol Parameters Derived From AERONET Retrievals Over the Arctic
Bibliographic record
Abstract
Abstract We investigated the climatological‐scale, monthly binned, seasonal variation of AERONET/Dubovik retrievals across six stations in the North American and European Arctic (multiyear sampling periods ranging from 8 to 17 years). A robust, spring‐to‐summer (StoS) increase in the radius of the peak of the fine mode (FM) component of the particle size distribution (PSD) was observed for five of the six stations. The FM aerosol optical depth (AOD) and the FM effective radius at the individual stations showed, respectively, a negligible to moderate StoS decrease and a significant increase. This was interpreted as a trade‐off between the waning influence of smaller FM Arctic haze aerosols and the increasing influence of large FM smoke particles. A springtime, pan‐Arctic PSD peak in the 1.3 μm coarse mode (CM) bin was attributed to Asian dust. It was suggested that the increase in amplitude of a second (4–7 μm) CM peak from July to August at the low‐elevation coastal sites was influenced by wind‐induced sea salt. The CM AOD went through a StoS decrease attributed to the decreasing amplitude of the 1.3 μm peak. A significant StoS CM effective radius increase was ascribed to the decreasing influence of the 1.3 μm peak. StoS FM fraction increases were largely due to the decrease of the CM AOD (decreasing influence of springtime Asian dust). This extensive and intensive climatology of remotely sensed, bimodal properties will, we believe, provide an important reference for future measurements and modeling of Arctic aerosols.
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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 | 0.001 | 0.001 |
| 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 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".