Aerial Seeding: An Effective Forest Restoration Method in Highly Degraded Forest Landscapes of Sub-Tropic Regions
Bibliographic record
Abstract
Carbon stock is an important indicator of cumulative ecosystem productivity. Using this indicator, and based on field sampling data, this paper compared the long-term difference in carbon stocks between aerial seeding (AS) and natural regeneration (NR) forests of Pinus massoniana in sub-tropic forests, China, in order to assess the effectiveness of AS in a highly degraded forest landscape. The results showed that the carbon stocks of stems, branches, roots, and trees (including stems, branches, leaves, and roots) were 140%, 85%, 110%, and 110%, significantly higher (p < 0.05) in the NR forests than those in the AS forests at the ages of 11–20 years, respectively. In addition, the carbon stocks of understory, litter and soil were also 176%, 151%, and 77%, significantly higher (p < 0.05) in the NR forests than those in the AS forests at the same age range, respectively. However, with increasing age (i.e., >21 years), those differences became statistically insignificant (p > 0.05). The total carbon stocks of the two forest types also showed a similar pattern. Those results clearly demonstrate that AS was an effective mean for restoring carbon stocks in highly degraded areas, even though their early growth was lower than the NR forests, and thus can be applied in the regions where the areas with limited seed sources and road accessibility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".