Comparison of N2O Emissions From Cold Waterlogged and Normal Paddy Fields
Bibliographic record
Abstract
Paddy fields are major sources of atmospheric N 2 O. Soil temperature and moisture strongly affect N 2 O emissions from rice fields. However, N 2 O emissions from cold-waterlogged paddy fields (CW), an important kind of paddy soil in China, are not well studied so far. It is unclear whether the N 2 O emissions from cold-waterlogged paddy fields are the same as normal paddy fields (NW). We investigated the N 2 O emission characteristics from the CW and NW paddy fields under with (R 1 ) and without (R 0 ) rice in Tuku Village, Baisha Town, Yangxin County (YX site, monitoring in 2013) and Huandiqiao Town, Daye City (DY site, monitoring in 2014); compared the difference and influencing factors between the CW and NW paddy fields at two sites in South China. The results showed that the N 2 O emissions from NWR 0 were 13.4 times higher than from CWR 0 , and from NWR 1 were 10.3 times higher than from CWR 1 in the YX site. The N 2 O emissions from NWR 0 were 2.4 times higher than from CWR 0 , and from NWR 1 were 17.3 times higher than from CWR 1 in the DY site. The structural equation models (SEMs) showed that the N 2 O emissions are mainly driven by rice planting and soil moisture in the NW fields at the annual scale, while soil temperature in the CW fields. Overall, N 2 O emissions from cold waterlogged paddy fields are significantly lower than those of normal paddy fields due to the low temperature and higher water content; however, there are dinitrogen emissions from cold waterlogged paddy fields denitrification should be further examined.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".