Vegetation change impacts on soil organic carbon chemical composition in subtropical forests
Bibliographic record
Abstract
Changes in the chemical composition of soil organic carbon (SOC) might strongly affect the global carbon cycle as it controls the SOC decomposition rate. Vegetation change associated with long-term land use changes is known to strongly impact the chemical composition of SOC; however, data on the impacts of vegetation change following disturbance events of short durations and succession that occur frequently in forest ecosystems via diverse management objectives on SOC chemical composition are negligible. Here we examined the impacts of vegetation changes on the chemical composition of SOC by sampling soils of native broad-leaved forests, planted mixed broad-leaved and coniferous forests, and tea gardens in eastern China. We used nuclear magnetic resonance spectroscopy to quantify SOC chemical composition. We found that among all components of SOC chemical composition, alkyl carbon (C) and aryl C were more liable to change with vegetation than other SOC components. Soil pH was negatively correlated to the relative abundances of alkyl C and N-alkyl C, and Shannon's index of overstory plant species was positively correlated to the relative abundances of phenolic C and aromaticity. Our results suggest that vegetation changes following short disturbance events and succession may strongly alter SOC chemical composition in forest ecosystems.
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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".