OMI/Aura, SCIAMACHY/Envisat and GOME2/MetopA Sulphur Dioxide Estimates; The Case of Eastern Asia
Bibliographic record
Abstract
The EU FP7 Monitoring and Assessment of Regional air quality in China using space Observations, Project Of Long-term sino-european co-Operation, MarcoPolo, project focuses on deriving emission estimates from space. \nLong term satellite observations of suphur dioxide, SO2, over the greater China area from the SCIAMACHY/Envisat, GOME2/MetopA and OMI/Aura missions are compared and their relative strong points and limitations are discussed. Rigorous spatiotemporal statistical analysis based on novel analysis techniques [Fioletov et al., 2011; 2013] is performed for each data set in order to reduce noise and biases and enhance pollution signals in satellite datasets. Furthermore, identification of point sources such as power plants, smelters and urban agglomerations, as well as definition of their relative contribution to the regional SO2 levels, form the main findings of this investigation. Comparison of different satellite datasets and their post-processed products with ground based MaxDOAS SO2 meas \nurements in Xianghe, China, located at ∼50 km southeast of downtown Beijing, helps validate the satellite datasets.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".