Perubahan Tutupan Lahan, Degradasi, dan Deforestasi Hutan di Kabupaten Nabire Periode 2000-2019
Bibliographic record
Abstract
Penelitian ini berfokus pada perubahan lanskap di Kabupaten Nabire, Papua. Penelitian ini menemukan tren perubahan penggunaan lahan, deforestasi dan degradasi selama dua periode, yaitu dari tahun 2000 hingga 2019. Penelitian ini menekankan pada pergeseran lahan dari hutan menjadi non-hutan, terutama pada periode awal di mana deforestasi dan perusakan hutan meningkat, dengan menggunakan data primer dan sekunder serta analisis menggunakan perangkat lunak SIG. Hasilnya menunjukkan dampak yang signifikan terhadap ekosistem dan lingkungan setempat. Hasil penelitian ini menunjukkan bahwa konservasi dan pengelolaan yang berkelanjutan sangat dibutuhkan untuk mengurangi kerusakan ekosistem hutan dan menjaga kelestarian lingkungan di Kabupaten Nabire. Penelitian ini juga membantu memahami perubahan lingkungan di daerah tersebut dan memberikan landasan untuk pengambilan keputusan dan implementasi kebijakan yang bertujuan untuk menjaga kelestarian ekosistem dan lingkungan di daerah tersebut.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".