Summer storms trigger soil N<sub>2</sub>O efflux episodes in forested catchments
Bibliographic record
Abstract
Abstract Climate change and climate‐driven feedbacks on catchment hydrology and biogeochemistry have the potential to alter the aquatic versus atmospheric fate of nitrogen (N) in forests. This study investigated the hypothesis that during the forest growth season, topography redistributes water and water‐soluble precursors (i.e., dissolved organic carbon and nitrate) for the formation of gaseous N species. Soil nitrous oxide (N2O) and nitrogen (N2) efflux and soil physical and chemical properties were measured in a temperate forest in Central Ontario, Canada from 2005 to 2010. Hotspots and hot moments of soil N2O and N2 efflux were observed in topographic positions that accumulate precipitation, which likely triggered the formation of redox conditions and in turn intercepted the conversion of nitrate N flowing to the stream by transforming it to N2O and N2. There was a strong relationship between precipitation and N2O efflux (y = 0.44x1.22, r2 = 0.618, p < 0.001 in the inner wetland; y = 1.30x1.16 r2 = 0.72, p < 0.001 in the outer wetland) and significantly different N2:N2O ratios in different areas of the wetland (19.6 in the inner wetland and 10.1 in the outer wetland). Soil N2O + N2 efflux in response to precipitation events accounted for 16.1% of the annual N input. A consequence of the higher frequency of extreme precipitation events predicted under climate change scenarios is the shift from an aquatic to atmospheric fate for N, resulting in a significant forest N efflux. This in turn creates feedbacks for even warmer conditions due to increased effluxes of potent greenhouse gases.
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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 | 0.000 |
| 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) | 0.001 | 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 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".