Soil Organic Carbon in Particle Size and Density Fractionations under Four Forest Vegetation-Land Use Types in Subtropical China
Bibliographic record
Abstract
Data on the effect of vegetation and land use type on soil organic carbon (SOC) distribution in particle-size and density fractions in the subtropical forest region in China will improve our understanding of the C sequestration potential of those different vegetation-land use types. We quantified SOC in particle size (coarse, medium and fine) and density fractions (light and heavy) under four types of common forest vegetation-land uses: an evergreen broad-leaf forest, a pine forest, a managed chestnut forest and an intensively managed bamboo forest in subtropical China. The SOC in the 0–20 and 20–40 cm soil layers was the highest in the bamboo forest (31.6–34.8 g·C·kg−1), followed by the evergreen broad-leaf forest (10.2–19.9 g·C·kg−1), the pine forest (8.5–13.6 g·C·kg−1) and the chestnut forest (6.3–12.2 g·C·kg−1). The SOC was largely in the coarse fraction under the evergreen broad-leaf, pine and bamboo forests, while it was largely in the fine fraction in the chestnut forest, suggesting that SOC in the chestnut forest was likely more stable than that in the other forest vegetation-land use types. The SOC in the light fraction under the four forest vegetation-land use types ranged from 1.4 to 13.1 g·C·kg−1 soil, representing 21%–37% of the total organic C; this suggests that the majority of the SOC was in the better protected, heavy fraction. We conclude that forest vegetation-land use type (and the associated management practices) influenced SOC distribution in particle size and density fractions in the studied subtropical forests in southeastern China.
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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 | 0.000 |
| 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 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".