Carbon Reserved Measurement as a Sustainability Strategy for Land Rehabilitation Program in Menoreh Hill Watershed, Central Java, Indonesia
Bibliographic record
Abstract
Watershed land rehabilitation is an important strategy to restore degraded land and improve ecosystem services, including carbon sequestration.However, increasing carbon reserves through watershed rehabilitation faces several challenges that need to be addressed.This study aims to measure the carbon reserve of land rehabilitation plants in the Bukit Menoreh watershed in order to make a strategic analysis of sustainable rehabilitation management.Quantitative research with experimental and descriptive approaches.Sampling using stratified random sampling.Primary data collection through field surveys.Durian and mangosteen plant species have the highest carbon reserves, with durian at 127.27 tons C and emission uptake of 467.07 tons CO2eq.Stand density and plant age significantly affected carbon reserves.The assumption of 100% plant survival results in an emission uptake of 640,960.73 tons CO2eq in 2040, higher than the 70% assumption of only 403,805.26 tons CO2eq.The results have important implications for future forest management and land rehabilitation policies, focusing on plant species selection, increasing stand density, and continuous monitoring of carbon reserves.The Menoreh watershed rehabilitation program can be an effective model for climate change mitigation and achieving sustainable development.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".