Stimulation of N<sub>2</sub>O emission via bacterial denitrification driven by acidification in estuarine sediments
Bibliographic record
Abstract
Abstract Ocean acidification in nitrogen‐enriched estuaries has raised global concerns. For decades, biotic and abiotic denitrification in estuarine sediments has been regarded as the major ways to remove reactive nitrogen, but they occur at the expense of releasing greenhouse gas nitrous oxide (N 2 O). However, how these pathways respond to acidification remains poorly understood. Here we performed a N 2 O isotopocules analysis coupled with respiration inhibition and molecular approaches to investigate the impacts of acidification on bacterial, fungal, and chemo‐denitrification, as well as N 2 O emission, in estuarine sediments through a series of anoxic incubations. Results showed that acidification stimulated N 2 O release from sediments, which was mainly mediated by the activity of bacterial denitrifiers, whereas in neutral environments, N 2 O production was dominated by fungi. We also found that the contribution of chemo‐denitrification to N 2 O production cannot be ignored, but was not significantly affected by acidification. The mechanistic investigation further demonstrated that acidification changed the keystone taxa of sedimentary denitrifiers from N 2 O‐reducing to N 2 O‐producing ones and reduced microbial electron‐transfer efficiency during denitrification. These findings provide novel insights into how acidification stimulates N 2 O emission and modulates its pathways in estuarine sediments, and how it may contribute to the acceleration of global climate change in the Anthropocene.
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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".