Cultivating Climate Solutions: Agroforestry’s Potentials and Roles in North Kalimantan’s REDD+ Program
Bibliographic record
Abstract
Agroforestry in North Kalimantan offers a promising avenue for balancing community livelihoods with carbon sequestration, crucial for the REDD+ initiatives. This paper examines the potential of agroforestry in North Kalimantan to support the REDD+ program, addressing both environmental sustainability and socio-economic development. Through field observations and interviews across four regencies and one city in North Kalimantan province, various agroforestry practices were identified, including improved fallows, alley cropping, scattered trees on cropland, living fences, and silvofishery. Challenges such as cultivation practices, post-harvest processing, market access, and financing were also explored. Three potential agroforestry models were proposed to enhance carbon capture while promoting local economic resilience. The paper underscores the importance of further research and community involvement to refine and expand these agroforestry approaches, offering hope for both local prosperity and global carbon reduction efforts.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".