PEMETAAN AREAL POTENSI KONFLIK IZIN USAHA PEMANFAATAN HASIL HUTAN KAYU HUTAN TANAMAN (IUPHHK HT) BERBASIS SISTEM INFORMASI GEOGRAFIS (SIG) PADA PT. RAPP ESTATE MANDAU
Bibliographic record
Abstract
PT. RAPP Estate Mandau,memiliki areal yang berkonflik pada konsesi adalah seluas + 6.339 ha dari areal konsesi seluas + 23.800 ha. Berdasarakan informasi tersebut diperlukan penanganan dan pengelolaan areal konflik dan areal potensi konflik. Penelitian ini bertujuan untuk memetakan daerah potensial konflik pada Izin Usaha Pemanfaatan Hasil Hutan Kayu Hutan Tanaman (IUPHHK -HT) PT. RAPP Estate Mandau. Metode yang digunakan dalam penelitian ini adalah analisis overlay dengan menggunakan teknologi sistem informasi geografis. Dalam penelitian ini dilakukan perhitungan komponen – komponen Jarak dari Jalan, Jarak dari Pemukiman, Tata Batas dan Penutupan Lahan. Hasil perhitungan tersebut kemudian diklasifikasikan terhadap tingkat potensial konflik yang terjadi dan dilakukan pemet aan wilayah terhadap tingkat potensial konflik yang debedakan berdasarkan warna dengan menggunakan Sistem Informasi Geografis. Hasil penelitian ini adalah bahwa tingkat potensial konflik di IUPHHK -HT PT RAPP Estate Mandau didominasi oleh kelas tidak potensial dengan luas 12.499,87 Ha (71,59%), untuk kelas potensial dengan luas 3.047,43 Ha (17,45%) dan untuk kelas sangat potensial seluas 1.913,70 Ha (10,96%) . Kata Kunci : konflik, sig, tutupan lahan.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.080 | 0.019 |
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".