Soil nitric and nitrous oxide emissions from agricultural and tidal flat fields in southwestern Korea
Bibliographic record
Abstract
A closed chamber system was used to measure fluxes of NO and N2O from soil surfaces. The research sites were located in southwestern Korea, and measurements were conducted during summer 2000 to assess the NO source strength of agricultural soils (upland and rice paddy) and tidal flats. Research for tidal flat soil fluxes was initially attempted to investigate their significance on the nitrogen budget in Korea. Soil samples were taken on each experimental day and analyzed for soil pH, soil moisture, and total Kjeldahl nitrogen (TKN = organic nitrogen + NH4-N + NO3-N). Soil nitrogen and NO flux increased significantly after applying nitrogen fertilizer to agricultural fields. Average NO and N2O fluxes were 84.8 ng N m2 s1 (range: 0.1~464.2 ng N m2 s1) and 67.8 ng N m2 s1 (range: 4.0~199.7 ng N m2 s1), respectively, from upland soils planted with green onions. Average N2O flux from soils planted with soybean was 52.5 ng N m2 s1 (range: 4.0~463.2 ng N m2 s1). Based on statistical test, no significant difference in N2O fluxes between green onion and soybean field was observed (t-statistic value = 1.2299; t98,0.05 = 1.6606). Soil NO and N2O fluxes from rice paddies and tidal flats were significantly lower than those from uplands; anoxic condition due to water saturation could limit the gas productions by processing nitrate reduction. Key words: NO and N2O flux, biogenic emission, closed chamber technology, agricultural soils, tidal flat nitrogen emission.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".