Impacts of Forest Management Stargate on Longterm Carbon Sequestration and Storage in Plantation Forests: A Case Study in China
Bibliographic record
Abstract
Forests act as a vital carbon reservoir. Hence, optimizing carbon sequestration and storage is an approach to attaining carbon neutrality. The Three-North Shelterbelt Project (TNSP) is the largest planted forestry ecological project in China. Established in 1978, it has significantly improved the ecological environment in North China. However, its carbon sequestration capacity is not well understood. This study employed the Forest Simulation Optimization System to model the forest carbon storage in TNSP across 200 years (1978-2177). It was observed that in the scenario without logging (Scenario 1), carbon storage fluctuates alongside the natural growth and death of trees, with a peak value of 10.7 million tons. In the logging scenario (Scenario 2), i.e., harvesting trees when they are mature and replanting new trees, carbon storage is greater and sustains a steady level of 15.1 million tons. Drawing on this study's findings, suggestions were put forward to boost carbon sequestration and storage in the TNSP, as well as to incorporate TNSP's forest management into regional economic and environmental planning.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".