Hydrologic and Edaphic Controls on Soil Carbon Emission in Dongting Lake Floodplain, China
Bibliographic record
Abstract
Abstract Wetlands play a critical role in mitigating carbon emission. However, little is known about soil carbon emission and their environmental controls from inland floodplain wetlands. This study aimed to determine the effects of hydrologic and edaphic controllers (water table depth [ WTD ], soil temperature [ Ts ], and soil water content [ SWC ]) on soil C emission in Dongting Lake wetland, China. One‐year emissions were measured in Carex meadow and mudflat using static chambers during 2013 to 2014, including nonflooded season ( NFs ) and flooded season ( Fs ). The results showed that soil C emission in the Carex meadow and mudflat was 307.8 and 264.3 g C·m −2 ·year −1 , respectively, and 50–66% of soil C were emitted during NFs. Compared with NFs, CO 2 emission was significantly decreased by 57% but CH 4 emission was significantly increased by 38 times during Fs in the Carex meadow. Stepwise regression combined with structural equation model analysis showed that CO 2 and CH 4 flux were mainly influenced by Ts during NFs, and they were controlled by water temperature ( Tw ) during Fs. During NFs, CO 2 flux increased with increasing Ts and SWC but decreased significantly when SWC was over 66% and 52% in the Carex meadow and mudflat, respectively. CH 4 flux showed an emission pulse at SWC and Ts of 65% and 17.2 °C, respectively. These results indicate that flooding significantly inhibited soil CO 2 emission but stimulated CH 4 emission. The continuous decrease of flooding days caused by anthropogenic disturbances may induce soil C loss in Dongting Lake wetlands.
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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".