Effects of Stand Origin and Near-Natural Restoration on the Stock and Structural Composition of Fallen Trees in Mid-Subtropical Forests
Bibliographic record
Abstract
Fallen trees comprise an important part of forest ecosystems and serve a central role in maintaining the biodiversity and tree regeneration of forests. However, the effects of stand origin and near-natural restoration on the biomass and carbon stock of fallen trees remain unclear. Based on 60 sampling plots of field surveys of mid-subtropical forests in Jiangxi Province, we investigated the stock and structural composition of fallen trees in artificial coniferous forests (Acf), natural coniferous forests (Ncf) (e.g., different stand origins) and natural evergreen broadleaf forests (Nebf) (e.g., near-natural restoration). The following results were obtained: (1) the largest biomass and carbon stocks of fallen trees among three forest types (Nebf, Ncf and Acf) were measured for Nebf; (2) the fallen tree biomass and carbon stock in natural Cunninghamia lanceolata forest (Nclf) were significantly larger than that in artificial Cunninghamia lanceolata forest (Aclf), and the fallen tree biomass and carbon stock in natural Pinus massoniana forest (Npf) were also significantly larger than those in artificial Pinus massoniana forest (Apf); (3) the diameter class allocation in natural forests was more uniform than that in artificial forests; (4) the biomass of fallen trees with mild decay was not significantly different among forest types within stand origin or among the stand origin within forest types; however, the biomass of fallen trees with moderate and heavy decay significantly differed among stand origin (Aclf vs. Nclf, Apf vs. Npf), but was not significant among the forest types (Aclf vs. Apf, Nclf vs. Apf) within a stand origin. Our results suggested that the large biomass and carbon stock of fallen trees in Nebf may serve a significant role in mitigating global warming and carbon cycles in mid-subtropical forests. Therefore, stand origin and near-natural restoration exert significant effects on the carbon stock and structural composition of fallen trees in mid-subtropical forests.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".