Divergent Responses of CH<sub>4</sub> Emissions and Uptake to Global Change Drivers
Bibliographic record
Abstract
Abstract Global changes strongly affect methane (CH 4 ) emissions and uptake. However, it is unclear how CH 4 emissions and uptake across rice paddy fields, uplands, and natural wetlands are affected by global change drivers, including nitrogen (N) addition, elevated carbon dioxide (eCO 2 ), warming (W), and precipitation (P). Here, we collected 1,250 observations of manipulated experiments from 303 publications during 1980–2020, encompassing 1,154 observations of single‐factor experiments and 96 observations of two‐paired experiments, and analyzed the effects of global change drivers on CH 4 emissions and uptake. Results showed CH 4 emissions were stimulated by eCO 2 , W, and increased P (IP). CH 4 uptake was inhibited by N and IP but significantly enhanced by W and decreased P. The combined effects of the four global change drivers significantly inhibited CH 4 uptake (−9[−12, −6] %) and stimulated CH 4 emissions (13[7, 19] %). Two‐factor interactions significantly reduced CH 4 emissions (−15[−27, −1] %) and insignificantly reduced uptake (−10[−19, 0] %). The interactive effects of any two global change drivers were mostly antagonistic. Random forest analysis indicated that the important factors affecting the responses of CH 4 emissions or uptake to different global change drivers varied. The structural equation model confirmed that climate, soil properties, and wetness index consistently played a remarkable role in regulating the responses of CH 4 emissions and uptake to global change drivers. This synthesis highlights an urgent need to consider the individual and interactive effects of multiple global change drivers on CH 4 emissions and uptake for a better understanding of the methane‐climate feedback.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 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".