Short- and long-term temperature responses of soil denitrifier net N <sub>2</sub> O efflux rates, inter-profile N <sub>2</sub> O dynamics, and microbial genetic potentials
Bibliographic record
Abstract
Abstract. Production and reduction of nitrous oxide (N2O) by soil denitrifiers influence atmospheric concentrations of this potent greenhouse gas. Accurate projections of the net N2O flux have three key uncertainties: (1) short- vs. long-term responses to warming, (2) interactions among soil horizons, and (3) temperature responses of different steps in the denitrification pathway. We addressed these uncertainties by sampling soil from a boreal forest climate transect encompassing a 5.2 ∘C difference in the mean annual temperature and incubating the soil horizons in isolation and together at three ecologically relevant temperatures in conditions that promote denitrification. Both short-term exposure to warmer temperatures and long-term exposure to a warmer climate increased N2O emissions from organic and mineral soils; an isotopic tracer suggested that an increase in N2O production was more important than a decline in N2O reduction. Short-term warming promoted the reduction of organic horizon-derived N2O by mineral soil when these horizons were incubated together. The abundance of nirS (a precursor gene for N2O production) was not sensitive to temperature, whereas that of nosZ clade I (a gene for N2O reduction) decreased with short-term warming in both horizons and was higher from a warmer climate. These results suggest a decoupling of gene abundance and process rates in these soils that differs across horizons and timescales. In spite of these variations, our results suggest a consistent, positive response of denitrifier-mediated net N2O efflux rates to temperature across timescales in these boreal forests. Our work also highlights the importance of understanding cross-horizon N2O fluxes for developing a predictive understanding of net N2O efflux from soils.
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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".