[Variation of forest vegetation carbon storage and carbon sequestration rate in Liaoning Province, Northeast China].
Bibliographic record
Abstract
The forest vegetation carbon stock and carbon sequestration rate in Liaoning Province, Northeast China, were predicted by using Canadian carbon balance model (CBM-CFS3) combining with the forest resource data. The future spatio-temporal distribution and trends of vegetation carbon storage, carbon density and carbon sequestration rate were projected, based on the two scenarios, i. e. with or without afforestation. The result suggested that the total forest vegetation carbon storage and carbon density in Liaoning Province in 2005 were 133.94 Tg and 25.08 t x hm(-2), respectively. The vegetation carbon storage in Quercus was the biggest, while in Robinia pseudoacacia was the least. Both Larix olgensis and broad-leaved forests had higher vegetation carbon densities than others, and the vegetation carbon densities of Pinus tabuliformis, Quercus and Robinia pseudoacacia were close to each other. The spatial distribution of forest vegetation carbon density in Liaoning Province showed a decrease trend from east to west. In the eastern forest area, the future increase of vegetation carbon density would be smaller than those in the northern forest area, because most of the forests in the former part were matured or over matured, while most of the forests in the later part were young. Under the scenario of no afforestation, the future increment of total forest vegetation carbon stock in Liaoning Province would increase gradually, and the total carbon sequestration rate would decrease, while they would both increase significantly under the afforestation scenario. Therefore, afforestation plays an important role in increasing vegetation carbon storage, carbon density and carbon sequestration rate.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".