Spatial and Temporal Variation in Soil Nitrous Oxide Emissions from a Rehabilitated and Undisturbed Riparian Forest
Bibliographic record
Abstract
Riparian zones enhance water quality and provide wildlife habitat, but high nutrient input in agricultural landscapes causes nitrous oxide (N2O) emissions, potentially negating their benefits of C sequestration. The objectives of this study were to quantify spatiotemporal N2O emissions in a rehabilitated and undisturbed natural riparian forest. We also determined soil and vegetation characteristics, and their role in driving spatiotemporal N2O emissions. Mean N2O‐N emissions were not significantly (p < 0.05) different between rehabilitated (7.62 μg m−2 h−1) and undisturbed (5.93 μg m−2 h−1) riparian forests. The greatest (p < 0.05) N2O‐N emissions in both riparian forests were observed during the summer. Soil moisture, temperature, and N were significantly correlated to N2O‐N emissions. Our results show that soil and vegetation characteristics varied between the two riparian forests, but differences in N2O‐N emissions were negligible. We also found that N2O emissions were influenced by soil characteristics and seasonality, rather than vegetation characteristics or spatial position. Core Ideas N2O emissions are influenced temporally rather than spatially in riparian zones. Rehabilitated and undisturbed riparian forests have similar N2O emissions. Seasonality and soil characteristics had a greater influence on N2O emissions than vegetation. Riparian systems generated hot moments rather than hot spots of N2O emissions.
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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) | 0.000 | 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".