Tata Kelola Taman Hutan Raya Nipa-Nipa
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
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.
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 | 0.000 | 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 | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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