ANALAISIS KELEMBAGAAN DAN PERANAN KESATUAN PENGELOLAAN HUTAN PRODUKSI (KPHP) DALAM PENGEMBANGAN WILAYAH KABUPATEN KERINCI
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
Kerinci is one of regency with the large forest, but sub sector of forestry contributes only 0,04% of GDPKerinci Regency. It’s may possibly by the weakness of forest management and policy of Kerinci RegencyGovernment. Forest production management unit (KPHP) Model Kerinci establishment is one of govermentefforts to achieve sustainable forest management. Therefore, we need research with purpose: (1) to analyzethe role of forest production management unit (KPHP) Model Kerinci in the regional development ofKerinci Regency; (2) to analyze the institutional of forest production management unit (KPHP) ModelKerinci; (3) to analyze region’s readiness forest production management unit (KPHP) Model Kerincidevelopment. The study was conducted in Kerinci Regency. Data were analyzed by total economic value(TEV), institutional analysis, and analytical hierarchy process (AHP). The results showed that the totaleconomic value of natural resources of KPHP Model Kerinci is Rp. 337.839.832.400 in a year, it’s meanthat sub sector of forestry potentially to contribute about 8,38% of GDP Kerinci Regency. To realize thetotal economic values of natural resources of KPHP Model Kerinci, it needs strong institutions. KerinciRegency is ready for KPHP Model Kerinci development, because it’s has the support from stakeholders.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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