First Results of Absorbing Aerosol Index From the Absorbing Aerosol Sensor Onboard Gaofen-5B
Bibliographic record
Abstract
The Absorbing Aerosol Index (AAI) determines the presence and approximate amount of absorbing aerosol particles. The Absorbing Aerosol Sensor (AAS) equipped on the Gaofen-5B satellite was successfully launched and deployed in orbit on September 7, 2021. Following a period of initial on-orbit testing, reliable AAI observational results were collected. The nadir of the AAS had a design spatial resolution of 4 km × 4 km and a current spatial resolution of 2 km × 4 km, which presented significant benefits in the spatial resolution and clear distribution characteristics of pollutants. The results of AAS were validated by comparing with the observed data of Global Ozone Monitoring Experiment-2 (GOME-2) in the Sahara Desert and surrounding areas. In 2022 and 2023, we utilized AAS observations to study the typical pollution processes. These included the spring pollution distribution around the Bohai Sea, the observation and tracking of long-term dust pollution in China in March and April 2023, the distribution of spring pollution in the Indochina Peninsula in April 2023, and the distribution of wildfires in Canada on June 22, 2023. The results were validated and analyzed using data from Tropospheric Monitoring Instrument (TROPOMI), GOME-2, AErosol RObotic NETwork (AERONET) and Moderate-resolution Imaging Spectroradiometer (MODIS). The results of the observation and evaluation revealed that the AAS AAI can provide reliable information for precise observation of air pollutants at a high resolution.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".