Soil Aggregation and Organic Carbon Dynamics in Poplar Plantations
Bibliographic record
Abstract
Soil resident water-stable macroaggregates (diameter (Ø) > 0.25 mm) play a critical role in organic carbon conservation and fertility. However, limited studies have investigated the direct effects of stand development on soil aggregation and its associated mechanisms. Here, we examined the dynamics of soil organic carbon, water-stable macroaggregates, litterfall production, fine-root (Ø < 1 mm) biomass, and soil microbial biomass carbon with stand development in poplar plantations (Populus deltoides L. ‘35’) in Eastern Coastal China, using an age sequence (i.e., five, nine, and 16 years since plantation establishment). We found that the quantity of water-stable macroaggregates and organic carbon content in topsoil (0–10 cm depth) increased significantly with stand age. With increasing stand age, annual aboveground litterfall production did not differ, while fine-root biomass sampled in June, August, and October increased. Further, microbial biomass carbon in the soil increased in June but decreased when sampled in October. Ridge regression analysis revealed that the weighted percentage of small (0.25 mm ≤ Ø < 2 mm) increased with soil microbial biomass carbon, while that of large aggregates (Ø ≥ 2 mm) increased with fine-root biomass as well as microbial biomass carbon. Our results reveal that soil microbial biomass carbon plays a critical role in the formation of both small and large aggregates, while fine roots enhance the formation of large aggregates.
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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 | 0.000 |
| 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".