Aerosol optical, microphysical, chemical and radiative properties of high aerosol load cases over the Arctic based on AERONET measurements
Bibliographic record
Abstract
Abstract Columnar mass concentrations of aerosol components over the Arctic are estimated using microphysical parameters derived from direct sun extinction and sky radiance measurements of Aerosol Robotic Network. Aerosol optical, microphysical, chemical and radiative properties show that Arctic aerosols are dominated by fine mode particles, especially for high aerosol load cases. The average aerosol optical depth (AOD) of the selected Arctic sites in the sampling period is approximately 0.08, with 75% composed of fine mode particles. The fine mode fraction mostly exceeds 0.9 when AOD greater than 0.4. The ammonium sulfate-like component (AS) contributes about 68% of total dry aerosol mass for high-AOD events. The estimated compositions and back trajectories show that the transported aerosol particles from biomass burning events have large amounts of black carbon (BC) and brown carbon, while those from pollution events are characterised by large AS fractions. The instantaneous radiative forcing at the top-of-atmosphere is higher for the more absorbing components, and varies greatly with surface albedo and solar zenith angle. A regression model of columnar composition and radiative forcing within the atmosphere (RFATM) for Arctic aerosol is established, showing that BC dominates a positive RFATM with a high warming efficiency.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".