Valuasi Ekonomi Hutan Mangrove di Wilayah Pesisir Desa Boroko Kabupaten Bolaang Mongondow Utara Provinsi Sulawesi Utara
Bibliographic record
Abstract
Ekosistem hutan mangrove merupakan salah satu sumberdaya alam wilayah pesisir yang mempunyai fungsi dan manfaat sangat besar, antara lain secara fisik, biologis, dan ekonomi, dengan fungsi utama sebagai penyeimbang ekosistem dan penyedia berbagai kebutuhan hidup bagi manusia dan mahluk hidup lainnya. Kabupaten Bolaang Mongondow Utara merupakan salah satu wilayah pesisir yang memiliki ekosistem mangrove di mana ekosistem hutan mangrove yang ada memiliki luas 1.670,81 Ha luas keseluruhan, di mana Desa Boroko merupakan salah satu desa potensi hutan mangrove dengan jumlah luas persebaran sebesar 101 Ha, yang mengalami degradasi antara lain di beberapa titik telah dialih fungsikan untuk kegiatan perkebunan cengkeh, penebangan yang dijadikan kayu bakar, pembukaan jalan, tambak, dan permukiman dengan luas indikatif kerusakan sebesar 4 Ha. Penelitian ini bertujuan untuk mengetahui nilai ekonomi total hutan mangrove setelah dipetakan tingkat kerusakan dan memperhitungkan nilai pemulihannya. Dengan metode analisis yang digunakan antara lain analisis tingkat kerusakan menggunakan metode NDVI (Normalized Difference Vegetation Index), dan analisis nilai ekonomi total kawasan menggunakan metode analisis kuantitatif dengan pendekatan valuasi ekonomi. Hasil penelitian menunujukan nilai manfaat total hutan mangrove di Desa Boroko sebesar Rp.261.210.638.132.-/Tahun.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".