A Model to Estimate Stored Carbon in the Upland Forests of the Wanggu Watershed
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Bibliographic record
Abstract
Climate change coupled with deforestation has brought about an increase in greenhouse gas emissions in the atmosphere. One way to control climate change is to reduce greenhouse gas emissions by maintaining the integrity of natural forests and increasing the density of tree populations. This research aimed to (a) identifies the density of stand trees in the upland forests of the Wanggu Watershed; (b) analyze the potential carbon stocks contained in the upstream forests of the Wanggu Watershed; (c) develop a model to estimate potential carbon stocks in the upland forests of the Wanggu Watershed. The land cover classification in this study used the guided classification with the Object-Based Image algorithm. Normalized Difference Vegetation Index (NDVI) was employed as an indicator of vegetation cover density. Field measurements were carried out by calculating the diameter of the stand trees in 30 observation plots. Field biomass values were obtained through allometric equations. Regression analysis was conducted to determine the correlation between NDVI densities and field biomass. The results showed that the best equation for estimating potential carbon stocks in the Wanggu Watershed forest area was y = 3.48 (Exp. 7,435x), with an R2 of 50.2%. Potential above ground biomass carbon in the Wanggu Watershed based on NDVI values was 414,043.26 tons in 2019, consist of protected forest areas of 279,070.15 tons and production forests of 134,973.11 tons. While total above biomass carbon based on field measurement reached 529,541.01 tons, consist of protected forests of 419,197.82 tons and production forests of 110,343.20 tons.
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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 | 0.000 | 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