N <sub>2</sub> O emissions from an irrigated and non‐irrigated organic soil in eastern Canada as influenced by N fertilizer addition
Bibliographic record
Abstract
Drainage and cultivation of organic soils often result in large nitrous oxide (N 2 O) emissions. The objective of this study was to assess the impacts of nitrogen (N) fertilizer on N 2 O emissions from a cultivated organic soil located south of Montréal, QC, Canada, drained in 1930 and used since then for vegetable production. Fluxes of N 2 O were measured weekly from May 2004 to November 2005 when snow cover was absent in irrigated and non‐irrigated plots receiving 0, 100 or 150 kg N ha −1 as NH 4 NO 3 . Soil mineral N content, gas concentrations, temperature, water table height and water content were also measured to help explain variations in N 2 O emissions. Annual emissions during the experiment were large, ranging from 3.6 to 40.2 kg N 2 O‐N ha −1 year −1 . The N 2 O emissions were decreased by N fertilizer addition in the non‐irrigated site but not in the irrigated site. The absence of a positive influence of soil mineral N content on N 2 O emissions was probably in part because up to 571 kg N ha −1 were mineralized during the snow‐free season. Emissions of N 2 O were positively correlated to soil CO 2 emissions and to variables associated with the extent of soil aeration such as soil oxygen concentration, precipitation and soil water table height, thereby indicating that soil moisture/aeration and carbon bioavailability were the main controls of N 2 O emission. The large N 2 O emissions observed in this study indicate that drained cultivated organic soils in eastern Canada have a potential for N 2 O‐N losses similar to, or greater than, organic soils located in northern Europe.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".