Tata Kelola Taman Hutan Raya Nipa-Nipa
Bibliographic record
Abstract
This study aims to analyze the physical condition of the environment, governance, supporting factors and limiting factors that affect the governance and direction of land use in Nipa-Nipa Forest Park (Tahura). The method used was a survey method with simple random sampling techniques and data collection techniques through documentation, interviews and surveys. Data analysis was performed using descriptive analysis and map overlay techniques.The results showed that the physical conditions of Nipa-Nipa Forest Park were unsuitable for residential and agricultural areas because its topography > 25 percent (steep) and it has high rainfall which causes erosion, landslides and floods. Types of land use by Nipa-Nipa community include harvesting (timber), gardening and settlement. Governance issues of Nipa-Nipa Forest Park include boundary management, area management planning employing the block division system with an active- participation approach based on local wisdom and environmental sustainability.Factors supporting the management of Nipa-Nipa Forest Park include the availability of water catchment areas, endemic flora and fauna, and natural tourist attractions. The limiting factor includes the geologically steep land, low level of public awareness and weak law enforcement. Concrete steps taken to promote justice and sustainable Nipa-Nipa community include provision of job opportunities, provision of support for micro bussiness, resettlement to safer and profitable areas, mentoring and provision of support for productive bussiness, and coaching and mentoring on agroforestry management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".