Methane emissions from rice paddies natural wetlands, lakes in China: synthesis new estimate
Bibliographic record
Abstract
Sources of methane (CH4 ) become highly variable for countries undergoing a heightened period of development due to both human activity and climate change. An urgent need therefore exists to budget key sources of CH4 , such as wetlands (rice paddies and natural wetlands) and lakes (including reservoirs and ponds), which are sensitive to these changes. For this study, references in relation to CH4 emissions from rice paddies, natural wetlands, and lakes in China were first reviewed and then reestimated based on the review itself. Total emissions from the three CH4 sources were 11.25 Tg CH4 yr(-1) (ranging from 7.98 to 15.16 Tg CH4 yr(-1) ). Among the emissions, 8.11 Tg CH4 yr(-1) (ranging from 5.20 to 11.36 Tg CH4 yr(-1) ) derived from rice paddies, 2.69 Tg CH4 yr(-1) (ranging from 2.46 to 3.20 Tg CH4 yr(-1) ) from natural wetlands, and 0.46 Tg CH4 yr(-1) (ranging from 0.33 to 0.59 Tg CH4 yr(-1) ) from lakes (including reservoirs and ponds). Plentiful water and warm conditions, as well as its large rice paddy area make rice paddies in southeastern China the greatest overall source of CH4 , accounting for approximately 55% of total paddy emissions. Natural wetland estimates were slightly higher than the other estimates owing to the higher CH4 emissions recorded within Qinghai-Tibetan Plateau peatlands. Total CH4 emissions from lakes were estimated for the first time by this study, with three quarters from the littoral zone and one quarter from lake surfaces. Rice paddies, natural wetlands, and lakes are not constant sources of CH4 , but decreasing ones influenced by anthropogenic activity and climate change. A new progress-based model used in conjunction with more observations through model-data fusion approach could help obtain better estimates and insights with regard to CH4 emissions deriving from wetlands and lakes in China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".