Estimasi Cadangan Karbon Akibat Perubahan Tutupan Lahan di Kabupaten Kendal
Bibliographic record
Abstract
Greenhouse gas emissions are the main cause of global warming through the Green House Effect event. This occurs due to an increase in greenhouse gas emissions from various emission sources, one of which is land cover change. Land cover on the earth's surface has a fixed carbon stock value that contributes to storing carbon stocks through its absorption capacity. So, if there is a change in land cover from one land cover to another, it will cause the carbon stock of a land cover to change. Research on changes in carbon stocks due to land cover changes were carried out in Kendal Regency. This study uses Landsat image data from 2008, 2013, and 2018. The method used is image interpretation with guided classifications and Stock Difference for calculating changes in carbon stocks. The results obtained from this study are the changes in land cover in Kendal Regency in 2008-2018. The occurrence of land cover conversion will affect the carbon stock stored in each type of land cover in the Kendal Regency. Kendal Regency's carbon stock for 10 years has decreased. In the period 2008-2013, the total decline in carbon stocks was -4,305,193.29 tons C. This situation was caused because land cover in 2008 had more total carbon stock than the total carbon stock in 2013. Meanwhile, in the 2013- period 2018, there was a change in the carbon stock of -1,450,080.51 ton C, where the land cover in 2013 had more total carbon stock compared to the total carbon stock stored in 2018. The difference in total carbon stock in a certain year is influenced by the area of land cover as well as the determination of the carbon stock that each land cover has. The results of calculations regarding the estimated carbon stock in the Kendal Regency can then be considered for stakeholders and policymakers to develop plans and scenarios (land use directions) that support the mitigation of land-based greenhouse gas emissions to address environmental problems.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; both teacher heads agree on what is shown here.
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".