[Variation of forest vegetation carbon storage and carbon sequestration rate in Liaoning Province, Northeast China].
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 it