ANALAISIS KELEMBAGAAN DAN PERANAN KESATUAN PENGELOLAAN HUTAN PRODUKSI (KPHP) DALAM PENGEMBANGAN WILAYAH KABUPATEN KERINCI
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.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".