A CHINA ENVIRONMENTAL HEALTH PROJECT FACT SHEET Transboundary Air Pollution—Will China Choke On Its Success?
Bibliographic record
Abstract
It is difficult to understate the influence of China’s atmospheric pollution on the Asia Pacific region and beyond. Prevailing winds carry pollutants such as ozone, fine particulate matter, and mercury from continent to continent, and in this case, from Asia to North America. Although statistics on China’s dismal air quality are dated, anecdotal, or limited in scope (e.g., China has not publicly disclosed CO2 or mercury emissions data since 2001), when examined as a whole, overall air pollution trends indicate a growing economic and health threat both within and outside China. Although the regional impact of China’s air pollution has encouraged some cooperation, new data on the economic, environmental, and human health implications of China’s pollution on Northeast Asia and the western seaboard of the United States and Canada call for more serious efforts by global stakeholders to engage China on these issues. Coal, Cars, and Desertification The majority of China’s domestic and transboundary air pollution originates from the country’s heavy dependence on coal, which makes up about 70 percent of its energy mix. Despite efforts to diversify energy sources, China will remain dependent upon coal for the
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".