Estimation of nitrous oxide emissions from rice paddy fields using the DNDC model: a case study of South Korea
Bibliographic record
Abstract
Abstract The Denitrification-Decomposition (DNDC)-Rice is a mechanistic model which is widely used for the simulation and estimation of greenhouse gas emissions [nitrous oxide (N2O)] from soils under rice cultivation. N2O emissions from paddy fields in South Korea are of high importance for their cumulative effect on climate. The objective of this study was to estimate the N2O emissions and biogeochemical factors involved in N2O emissions such as ammonium (NH4+) and nitrate (NO3−) using the DNDC model in the rice-growing regions of South Korea. N2O emission was observed at every application of fertilizer and during end-season drainage at different rice-growing regions in South Korea. Maximum NH4+ and NO3− were observed at 0–10 cm depth of soil. NH4+ increased at each fertilizer application and no change in NO3− was observed during flooding. NH4+ decreased and NO3− increased simultaneously at end-season drainage. Minimum and maximum cumulative N2O emissions were observed at Chungcheongbuk-do and Jeju-do regions of South Korea, respectively. The simulated average cumulative N2O emission in rice paddies of South Korea was 1.37 kg N2O-N ha−1 season−1. This study will help in calculating the total nitrogen emissions from agriculture land of South Korea and the World.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".