Influence of aerially seeded <i>Pinus massoniana</i> plantations on soil quality in severely eroded and degraded land of subtropical China
Bibliographic record
Abstract
Vegetation restoration is widely used to reduce soil erosion and control soil degradation, which is conducive to improving soil quality. Aerial seeding is an effective vegetation recovery method that has been applied in large areas with severe soil erosion in China. Pinus massoniana is not only a typical native coniferous tree species, but also a pioneer tree species for vegetation recovery in subtropical China. This study evaluates the soil quality of aerially seeded P. massoniana plantations of different stand ages and examines the vegetation factors affecting soil quality. Principal component analysis and Pearson correlation analysis were used to determine the minimum data set (MDS) for developing a soil quality index. The relationship between soil quality and vegetation factors was analyzed using redundancy analysis. The MDS was established with soil bulk density, field water capacity, non-capillary porosity, total nitrogen, soil organic matter, and pH. The results showed that the soil quality significantly increased with vegetation recovery age at 0–20 and 20–50 cm soil depths. The soil quality of the surface layer was mainly affected by understory vegetation and litter, whereas that of the deep layer was mainly affected by trees. Therefore, the appropriate management of P. massoniana forest, achieved by appropriately extending forest management rotation, replanting broad-leaved trees, and minimizing the damage to understory vegetation and litter, is essential for effectively improving soil quality.
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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".