Temperature sensitivity of N<sub>2</sub>O emissions from fertilized agricultural soils: Mathematical modeling in ecosys
Bibliographic record
Abstract
N2O emissions have been found to be highly sensitive to soil temperature (Ts) which may cause substantial rises in emissions with rises in Ts expected in most climate change scenarios. Mathematical models used to project changes in emissions during climate change should be able to simulate the physical and biological processes by which this sensitivity is determined. We show that the large rises in N2O emissions with short‐term rises in Ts (Q10 > 5) found in controlled temperature studies can be modeled from established Arrhenius functions for rates of microbial C and N oxidation (Q10 ∼ 2) when combined with Ts effects on gaseous solubilities and diffusivities and with water effects on gaseous diffusivities, interphase gas transfer coefficients, and diffusion path lengths. Rises in N2O emissions modeled with a long‐term rise in Ts during a climate warming scenario were smaller than expected from short‐term rises in Ts. Nonetheless, annual N2O emissions rose by ∼30% during three growing seasons in a cool humid maize‐soybean rotation under a climate change scenario in which atmospheric CO2 concentration Ca was raised by 50%, air temperature Ta by 3°C, and precipitation events by 5%. These model results indicate that climate warming may cause substantial rises in N2O emissions from fertilized agricultural fields in cool, humid climates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".