Rehabilitasi hutan di Indonesia: akan kemanakah arahnya setelah lebih dari tiga dasawarsa?
Bibliographic record
Abstract
Rehabilitation activities in Indonesia have a long-history of more than three decades, implemented in more than 400 locations. Successful projects are characterised by the active involvement of local people, and the technical intervention used tailored to address the specific ecological causes of degradation that concern local people. However, sustaining the positive impacts beyond the project time is still the biggest challenge. Rehabilitation efforts have been lagging behind the increasing rates of deforestation and land degradation. This has been largely due to the complexities of the driving factors causing the degradation, which neither projects nor have other government programmes been able to simultaneously address. Currently, there are more complex driving factors of deforestation to be dealt with, such as illegal logging and forest encroachment. Therefore, addressing the causes of deforestation and land degradation, which usually are also the continuing disturbances threatening sustainable rehabilitation activities, should be part of the project's priorities. Designing the right economic and social incentives is important to stimulate greater community roles in rehabilitation initiatives. Project derived economic and livelihood benefits, generated from ecological improvements, tend to sustain in the long-term more than the benefits from project-based economic opportunities.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".