Methane Production Is More Sensitive to Temperature Increase than Aerobic and Anaerobic Methane Oxidation in Chinese Paddy Soils
Bibliographic record
Abstract
Methane emissions from paddy fields can increase under future warming scenarios. Nevertheless, a comprehensive comparison of the temperature sensitivity of methane-related microbial processes remains elusive. Here, we revealed that the temperature sensitivity of methane production (activation energy ( E a ) = 0.94 eV; 95% confidence interval (CI), 0.78–1.10 eV) and aerobic ( E a = 0.49 eV; 95% CI, 0.34–0.65 eV) and anaerobic ( E a = 0.46 eV; 95% CI, 0.30–0.62 eV) methane oxidation exhibited notable spatial heterogeneity across 12 Chinese paddy fields spanning 35° longitude and 18° latitude. In addition, the E a values of aerobic and anaerobic methane oxidation were significantly positively and negatively correlated to the latitude, respectively, while there was no significant correlation between the E a of methane production and the latitude. Overall, there were no soil factors that had a significant effect on the E a of methane production. The E a of aerobic methane oxidation was primarily influenced by the contents of ammonium and clay, whereas the E a of anaerobic methane oxidation was mainly influenced by the conductivity. Despite the variation, the overall temperature sensitivity of methane production was significantly higher than that of oxidation at a continental scale; therefore, an increase in the emission of methane from paddy fields will be predicted under future warming. Taken together, our study revealed the characteristics of temperature sensitivity of methane production and aerobic and anaerobic methane oxidation simultaneously in Chinese paddy fields, highlighting the potential roles of soil factors in influencing temperature sensitivity.
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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 | 0.001 | 0.001 |
| 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".