Vegetation composition modulates the interaction of climate warming and elevated nitrogen deposition on nitrous oxide flux in a boreal peatland
Bibliographic record
Abstract
Abstract Northern peatlands with large organic nitrogen (N) storage have the potential to be N2O hotspots under climate warming, elevated N deposition, and vegetation composition change caused by climate change. However, the interactions of these three factors and the primary controls on N2O fluxes in peatlands are not well‐known. Here, the three factors were manipulated in a boreal bog in western Newfoundland, Canada for 5 years. We found that warming mitigated the positive N effect on N2O fluxes in the mid‐growing season under intact vegetation owing to the increase of available N uptake by vegetation and less N for N2O production. In contrast, warming strengthened the N effect on N2O fluxes in the early growing season under the absence of graminoids or shrubs, which could be attributed to the increase of available carbon and nitrogen for N2O production. It should be noted that these effects were not observed under the condition of low carbon availability. In addition, gross primary production was found as a critical control on N2O fluxes under N addition. Our findings emphasize that the interaction of abiotic (warming and elevated nitrogen deposition) and biotic factors (vegetation composition change) on N2O fluxes should be taken into account in order to project N2O fluxes in peatland ecosystems accurately.
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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".