Stable soil organic carbon is positively linked to microbial-derived compounds in four plantations of subtropical China
Bibliographic record
Abstract
Abstract. Indigenous broadleaf plantations are increasingly being developed to substitute pure coniferous plantations to increase biodiversity and soil fertility in subtropical China. To assess how plantation types affect soil organic carbon (SOC) chemical composition, we used the solid-state 13C nuclear magnetic resonance spectroscopy with cross-polarization and magic-angle spinning (CPMAS-NMR) technique to analyze SOC and litter C chemical compositions in a coniferous (Pinus massoniana) and three broadleaf (Castanopsis hystrix, Michelia macclurei and Mytilaria laosensis) plantations in subtropical China. Soil microbial community composition and biomass were investigated with the phospholipid fatty acids (PLFAs) and chloroform fumigation-extraction methods, respectively. The SOC chemical composition varied with plantation type, with 34% of the SOC found in the alkyl C fraction in the P. massoniana plantation compared to < 28% in the broadleaf plantations. The amount of total PLFAs, bacterial and particularly the gram-positive bacterial population size, and microbial C / N ratio were correlated with the alkyl C content and alkyl C / O-alkyl C ratio. However, the soil alkyl C content was not correlated with the recalcitrance of leaf litter or fine roots. We thus suggest that the stable SOC composition could be attributable to the contribution of microbial-derived C compounds, rather than leaf litter or fine root quality or a direct flux of C from recalcitrant litter materials to more stable SOC pools.
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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".