Seasonality of temperature dependence of methane fluxes from natural wetlands
Bibliographic record
Abstract
Abstract Temperature dependence is a crucial parameter in estimating methane (CH4) fluxes from natural wetlands, yet our understanding of this parameter remains inadequate. Seasonal fluctuations in water levels and ecosystem productivity lead to seasonal differences in CH4 production and oxidation. We hypothesized the existence of seasonality in the temperature dependence of CH4 fluxes. To validate this hypothesis, we analysed the FLUXNET‐CH4 dataset to determine the seasonal variation in temperature dependence of CH4 fluxes. We divided the year into six seasons based on air temperature and assessed the temperature dependence for each season using the apparent activation energy calculated by the Boltzmann–Arrhenius equation. Our results showed that temperature dependence showed a unimodal trend with seasons, with the apparent activation energy peaking in early summer (0.60 eV) and reaching its lowest point in late winter (−0.02 eV). This seasonal pattern of temperature dependence was consistent across wetlands with different vegetation types and hydrological conditions. Modelling of global wetland CH4 emissions based on seasonal temperature dependences showed a 19% (4%–45%) increase in emission rates under the most severe temperature rise scenario. Our results emphasize the seasonality of temperature dependence, which will help to further improve current and future predictions of wetland CH4 emissions. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".