Observed Methane Uptake and Emissions at the Ecosystem Scale and Environmental Controls in a Subtropical Forest
Bibliographic record
Abstract
Methane (CH4) is one of the three most important greenhouse gases. To date, observations of ecosystem-scale methane (CH4) fluxes in forests are currently lacking in the global CH4 budget. The environmental factors controlling CH4 flux dynamics remain poorly understood at the ecosystem scale. In this study, we used a state-of-the-art eddy covariance technique to continuously measure the CH4 flux from 2016 to 2018 in a subtropical forest of Zhejiang Province in China, quantify the annual CH4 budget and investigate its control factors. We found that the total annual CH4 budget was 1.15 ± 0.28~4.79 ± 0.49 g CH4 m−2 year−1 for 2017–2018. The daily CH4 flux reached an emission peak of 0.145 g m−2 d−1 during winter and an uptake peak of −0.142 g m−2 d−1 in summer. During the whole study period, the studied forest region acted as a CH4 source (78.65%) during winter and a sink (21.35%) in summer. Soil temperature had a negative relationship (p < 0.01; R2 = 0.344) with CH4 flux but had a positive relationship with soil moisture (p < 0.01; R2 = 0.348). Our results showed that soil temperature and moisture were the most important factors controlling the ecosystem-scale CH4 flux dynamics of subtropical forests in the Tianmu Mountain Nature Reserve in Zhejiang Province, China. Subtropical forest ecosystems in China acted as a net source of methane emissions from 2016 to 2018, providing positive feedback to global climate warming.
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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) | 0.001 | 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 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".