Decadal Trends in Wet Sulfur Deposition in China Estimated From OMI SO<sub>2</sub> Columns
Bibliographic record
Abstract
Abstract Long‐term (from 2005 to 2016) trends in wet SO42− deposition across China are assessed using Ozone Monitoring Instrument (OMI) SO2 columns within the planetary boundary layer. The models for estimating monthly SO42− deposition from precipitation in eight ecological regions are constructed based on SO2 columns and ground‐based measurements. An accuracy assessment shows that the models achieve highly precise predictive values for monthly deposition (R = 0.93, with a relative/absolute error of −0.1/0.3 kg S · ha−1 · month−1). In terms of spatial distribution, SO42− deposition shows substantial variations across China, ranging from 0.9 to 63.9 kg S · ha−1 · year−1, with an average of 10.4 kg S · ha−1 · year−1. Additionally, wet SO42− deposition shows significant seasonal variations, increasing from January to July and then decreasing thereafter. Regarding long‐term trends, the wet SO42− deposition in northern, central, and southern China decreased at rates of 0.009, 0.001, and 0.0009 kg S · ha−1 · month−1, respectively. In contrast, Inner Mongolia, Qinghai‐Tibet, and northwest and northeast China showed increasing deposition trends. In general, the wet SO42− deposition in 2016 decreased by 4.3% from that in 2005 on a national scale, indicating that air quality policies to control SO2 emissions have had some effects on wet SO42− deposition.
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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 | 0.000 |
| 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".