Assessing and Forecasting Carbon Stock Variations in Response to Land Use and Land Cover Changes in Central Aceh, Indonesia
Bibliographic record
Abstract
Land use and land cover (LULC) changes driven by rapid urbanization and land use policies can result in changes in carbon stocks.The study conducted in the region of Central Aceh (TCAR) aims to 1) identify the pattern of LULC change in TCAR 2001-2019 and make predictions of its changes in 2039 using the CA-Markov simulation, and 2) quantify changes in carbon stocks in the region in 2001-2019 and their predicted changes in 2039.This study uses Landsat 5 (2001), Landsat 7 (2009) and Landsat 8 OLI /TIRS (2019) satellite imagery, which is classified into six LULC categories using the supervised classification method.The 2001-2019 LULC map from the classification results was then tested for accuracy.The CA-Markov model was used to predict LULC changes and to obtain carbon stock values related to changes in LULC patterns.This study provides a new understanding of changes in LULC and their impact on carbon stocks, where LULC changes result in a decrease in carbon stocks, and will continue to decrease in 2039, along with the continued decline in forest area.These findings can be valuable information for policymakers in determining the appropriate spatial configuration in the future, which places more emphasis on increasing nature and environmental conservation, to mitigate climate change, a healthy environment, and provide comfort for the community.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".