Carbon Reserved Measurement as a Sustainability Strategy for Land Rehabilitation Program in Menoreh Hill Watershed, Central Java, Indonesia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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