Forest biomass estimation at regional and global levels, with special reference to China's forest biomass
Bibliographic record
Abstract
Accurate estimation of forest biomass size and regional distribution is a prerequisite in answering a long‐standing debate on the role of forest vegetation in the regional and global carbon cycle. Appropriate biomass estimation methods and available forest data sources are two key factors for this purpose. Among the estimation methods, the continuous Biomass Expansion Factor (BEF; defined as the ratio of all stand biomass to stem volume or biomass) method is considered to be the best. We applied the continuous BEF to forest inventory data of China and estimated a biomass carbon of 4.6 PgC and a biomass carbon density of 38.4 Mg ha –1 . A review of recent literature shows that forest carbon density in major temperate and boreal forest regions in the Northern Hemisphere has a narrow variance ranging from 29 Mg ha −1 to 50 Mg ha −1 , with a global mean of 36.9 Mg ha −1 . This suggests that the forest biomass density in China is closely coincident with the global mean.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".