Vegetation-mediated surface soil organic carbon formation and potential carbon loss risks in Dongting Lake floodplain, China
Bibliographic record
Abstract
Abstract. Sources and stabilization mechanisms of soil organic carbon (SOC) fundamentally govern the carbon sequestration potential of wetland ecosystems. Nevertheless, systematic investigations regarding SOC sources and molecular stability remain scarce in floodplain wetland environments. This study employed dual analytical approaches (stable isotope analysis and 13C nuclear magnetic resonance spectroscopy) to characterize surface SOC composition across three dominant vegetation communities (Miscanthus, Carex, and mudflat) in Dongting Lake floodplain wetlands. Key findings revealed: (1) Significantly elevated SOC concentrations in vegetated communities (Miscanthus: 13.76 g kg-1; Carex: 12.98 g kg-1) compared to unvegetated mudflat (6.88 g kg-1); (2) Distinct δ13C signatures across communities, with the highest isotopic values in Miscanthus (−22.67 ‰), intermediate in mudflat (−26.01 ‰), and most depleted values in Carex (−28.25 ‰); (3) Bayesian mixing models identified autochthonous plant biomass as the primary SOC source (Miscanthus: 53.3±10.6 %, Carex: 52.4 %±11.6 %, Mudflat: 47.5±12.5 %); (4) Spatial heterogeneity in POM contributions across sub-lakes, showing descending contributions from South (highest) > West > East (lowest) Dongting Lake; (5) Molecular characterization revealed O-alkyl C dominance (27.3–46.8 %), followed by alkyl C and aromatic C. Notably, Miscanthus soils exhibited enhanced O-alkyl C content (Alip/Arom) and reduced aromaticity/hydrophobicity indices, suggesting comparatively lower biochemical stability of its SOC pool. These results highlight the critical role of vegetation-mediated SOC formation processes and warn against potential carbon loss risks in Miscanthus-dominated floodplain ecosystems, providing a scientific basis for carbon management of wetland soils.
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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 | 0.001 | 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".