Mapping Potential Carbon Stocks and CO₂ Emissions Due to Land Cover Change in the Wanggu Watershed
Bibliographic record
Abstract
Rapid land use change in the Wanggu Watershed also impacts the condition of carbon stocks and emissions.Geographic information systems have been widely used to estimate carbon uptake and storage for various types of land use, but research on carbon emissions and stocks as a result of land use change is still limited land use change is still limited, particularly including in the Wanggu Watershed.This study aims to determine the amount of carbon emissions and stocks as an impact of land use change in the Wanggu Watershed.The method used is the technique of overlaying time series data of land use and then an analysis of emissions and carbon stocks based on carbon stock coefficients based on land use.The results showed that land cover changes in the Wanggu Watershed have significantly impacted carbon stocks and CO₂ emissions.In 2022, the total carbon stock was recorded at 2,041,789 tonnes C, while emissions reached 5,015,794 tonnes CO₂, originating from nine land cover types, including dryland forests, secondary mangrove forests, plantations, agricultural lands, settlements, open land, and paddy fields.Between 1990 and 2022, these changes have substantially altered carbon dynamics, with forest degradation contributing 798,352 tonnes CO₂, a significantly larger share than deforestation, which accounted for 107,159 tonnes CO₂.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".