Nitrous oxide production and sources in response to a simulated fall-freeze-thaw cycle
Bibliographic record
Abstract
Thaw-induced N2O emissions have been shown to account for 30-90% of N2O emissions in agricultural fields. Due to the climate change, increased precipitatio is expected in fall and winter seasons for certain regions. As a result, this would in turn enhance the thaw-induced N2O emissions and aggravate climate change. A mesocosm study was conducted to investigate N2O production and sources from soils under elevated soil moisture contents in response to a simulated fall-freeze-thaw cycle. Treatments included two levels of N addition (urea versus control) and two different management histories [with (SW) and without (CT) manure additions]. Our results showed that at least 92% of the N2O emissions during the study were produced during the simulated thawing across all treatments. The thaw-induced N2O emissions increased with increasing soil water content. The fall-applied urea increased the soil-derived N2O emissions during thawing, indicating an excessive mineralization of soil organic N. Compared to the CT soils, the SW soils induced more soil-derived N2O emissions. This could be because the SW soil had more easily decomposable organic matter which was likely due to historical manure additions. Regarding to the daily primed N2O fluxes, different soil water contents impacted the dynamics of daily priming effect. At the high water content, the soils experienced a shift in daily primed N2O fluxes from positive to negative and eventually back to positive throughout the simulated thawing, while the soils at lower water contents underwent positive primed fluxes in general. The shift in daily primed fluxes was probably driven by the preference of soil microbes on the labile N substrates. When the microbes switched from easily to moderately decomposed substrates (e.g., from dissolved organic N to plant residuals), they started to uptake inorganic N from the soil due to a relatively high C:N ratio of plant residuals. Therefore, a net N immobilization and negative primed N2O production occur in the short term in the soils at the high water content.
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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 | 0.001 |
| 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".