Controls of soil and aggregate‐associated organic carbon variations following natural vegetation restoration on the <scp>L</scp>oess <scp>P</scp>lateau in <scp>C</scp>hina
Bibliographic record
Abstract
Abstract Natural vegetation restoration can enhance soil organic carbon (SOC) sequestration, but the mechanisms and control factors underlying SOC sequestration are still unknown. The objectives of the study are to quantify the temporal variation of soil and aggregate‐associated organic carbon (OC) and identify factors controlling the variation following natural vegetation restoration after farmland abandonment. We collected soils from sites having 5, 30, 60, 100, and 160 years of a natural vegetation restoration chronosequence after farmland abandonment in the Loess Plateau, China. The results showed that natural vegetation restoration increased macroaggregates (0.25–2 mm; 46.6% to 73.9%), SOC (2.27 to 9.81 g kg −1 ), and aggregate OC (7.33 to 36.98 g kg −1 ) in the top 20‐cm soil compared with abandoned farmland, and the increases mainly occurred in the early stage (<60 years). The increase of SOC was contributed by OC accumulated in macroaggregates (0.25–2 mm) rather than microaggregates (≤0.25 mm). Moreover, SOC sequestration in the topsoil (0–10 cm) was mainly determined by fine root biomass (FR), labile organic carbon (LOC), and microbial biomass carbon (MBC). And in the subsoil (10–20 cm), SOC sequestration was mainly determined by the proportion of macroaggregates. The results suggest that natural vegetation restoration increased SOC and aggregate OC, and FR, MBC, LOC, and the physical protection of aggregates played important roles in regulating SOC and aggregate OC.
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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 | 0.000 | 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 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".