Carbon fluxes and soil carbon dynamics along a gradient of biogeomorphic succession in alpine wetlands of Tibetan Plateau
Bibliographic record
Abstract
Hydrological changes under climate warming drive the biogeomorphic succession of wetlands and may trigger substantial carbon loss from the carbon-rich ecosystems. Although many studies have explored the responses of wetland carbon emissions to short-term hydrological change, it remains poorly understood how the carbon cycle evolves with hydrology-driven wetland succession. Here, we used a space-for-time approach across hydrological gradients on the Tibetan Plateau to examine the dynamics of ecosystem carbon fluxes (carbon dioxide (CO2) and methane (CH4)) and soil organic carbon pools during alpine wetland succession. We found that the succession from mesic meadow to fen changed the seasonality of both CO2 and CH4 fluxes, which was related to the shift in plant community composition, enhanced regulation of soil hydrology and increasing contribution of spring-thaw emission. The paludification caused a switch from net uptake of gaseous carbon to net release on an annual timescale but produced a large accumulation of soil organic carbon. We attempted to attribute the paradox between evidence from the carbon fluxes and pools to the lateral carbon input and the systematic changes of historical climate, given that the wetlands are spatially low-lying with strong temporal climate-carbon cycle interactions. These findings demonstrate a systematic change in the carbon cycle with succession and suggest that biogeomorphic succession and lateral carbon flows are both important for understanding the long-term dynamics of wetland carbon footprints.
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 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) | 0.001 | 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".