Effects of soil water content at freezing, thaw temperature, and snowmelt infiltration on N2O emissions and denitrifier gene and transcript abundance during a single freeze-thaw event
Bibliographic record
Abstract
Abstract Climate change-related warming and increased precipitation may alter winter snow cover and thawing events, and therefore, may carry significant consequences for nitrous oxide (N 2 O) production pathways such as denitrification, and the abundance and expression of denitrifying microorganisms. We used a soil microcosm study to investigate the combined effect of soil thaw temperature, initial water filled pore space (WFPS) prior to soil freezing, and snowmelt infiltration simulated by the addition of water on N 2 O emission and denitrification rates, soil respiration rate, and the abundance and transcription of denitrifying ( nirK , nirS , and nosZ ) bacteria during a single freeze-thaw event. Soil respiration rate was primarily controlled by an increase in soil thaw temperature, whereas soil N 2 O emission and denitrification rates were generally greater in soils with a higher initial WFPS and soil thaw temperature. In contrast, snowmelt infiltration generally had a negligible effect on these rates, which may be related to pre-existing soil conditions that were already conducive to denitrification. Unexpectedly, the nosZ transcript/ nosZ gene abundance ratio was lower in soils thawed at 8.0 °C compared to 1.5 °C; however, this may have resulted in a lower N 2 O reduction, thus explaining the greater levels of N 2 O emitted from soils thawed at 8.0 °C. Overall, this study demonstrated that increased N 2 O production during a single freeze-thaw event was primarily linked to antecedent conditions of high initial WFPS, soil thaw temperature, and a synergistic interplay between these two environmental parameters, and provides evidence that an increase in annual temperature and precipitation, along with the timing of precipitation, may further stimulate N 2 O production pathways.
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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".