Estimating carbon stocks in Korean forests between 2010 and 2110: a prediction based on forest volume–age relationships
Bibliographic record
Abstract
This study was focused on attempting to estimate the potential change in forest carbon stocks between 2010 and 2110 in South Korea, using forest cover maps and National Forest Inventory (NFI) data. Allometric functions (logistic regression models) of volume–age relationships were developed to estimate carbon stock change during the next 100 years for Pinus densiflora, P. koraiensis, P. rigida, Larix kaempferi and Quercus spp. As a result, we found that the average forest volume would increase from 126.89 m3/ha to 246.61 m3/ha and the average carbon stocks would increase from 50.51 Mg C/ha to 99.76 Mg C/ha during the next 100 years. The carbon stocks would increase by approximately 0.5 Mg C/(ha·yr), a high value if other northern countries’ (Canada, Russia, China, etc.) rates of increase are considered, as these are −0.10 to 0.28 Mg C/(ha·yr) as determined in a previous study. This can probably be attributed to the fact that the change in carbon stocks was estimated without the consideration of mortality, thinning, and tree species’ change in this study, which is may lead to somewhat overestimation of carbon sequestration. However, this study is meaningful, as the estimated carbon stocks were based on the data from NFI and forest cover maps.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".