Seasonality of temperature dependence of methane fluxes from natural wetlands
Bibliographic record
Abstract
Abstract Temperature dependence is a crucial parameter in estimating methane (CH 4 ) 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 CH 4 production and oxidation. We hypothesized the existence of seasonality in the temperature dependence of CH 4 fluxes. To validate this hypothesis, we analysed the FLUXNET‐CH4 dataset to determine the seasonal variation in temperature dependence of CH 4 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 CH 4 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 CH 4 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.004 | 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 teacher head, 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".