Evaluation of anthropogenic emissions of carbon monoxide in East Asia derived from the observations of atmospheric radon-222 over the western North Pacific
Bibliographic record
Abstract
Abstract. We used the observed CO/222Rn ratio in the Asian outflows at Minamitorishima (MNM), Yonagunijima (YON), and Ryori (RYO) in the western North Pacific from 2007 to 2011, together with a three-dimensional chemical transport model (STAG), in order to estimate anthropogenic emissions of CO in East Asia. The measurements captured high-frequency synoptic variations of enhanced 222Rn (ERN) events associated with the long-range transport of continental air masses. 222Rn and CO showed high correlation during the ERN events observed at MNM and YON in the winter and spring, but not at RYO. The STAG transport model reproduced well the concentrations of observed 222Rn when forced with a constant and uniform flux density of 1.0 atom cm−2 s−1, but underestimated the associated enhancement of synoptically variable CO caused by the underestimated flux values in the EDGAR ver. 4.1 emission database used in the model for East Asia. Better estimates for the East Asian emission were derived using a radon tracer method based on the difference in the enhancement ratio of CO/222Rn between the observation and the model. The anthropogenic emissions of CO for China, Japan, and Korea were estimated to be 203 Tg CO yr−1, 91% of which originated in China. When compared with other estimated emissions of CO, our estimated result showed consistency with those of the inverse method, whereas the emission database of EDGAR was about 45% smaller than our anthropogenic estimation for China.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".