Analisis Spasial Kesesuaian Fungsi Kawasan Daerah Aliran Sungai Bangop Dengan Rencana Tata Ruang Wilayah Kabupaten Tapanuli Tengah (Studi Kasus: Kecamatan Sarudik)
Bibliographic record
Abstract
Tujuan penelitian untuk mengetahui tingkat kesesuaian fungsi kawasan eksisting Daerah Aliran Sungai Bangop di Kecamatan Sarudik dengan rencana pola ruang RTRW Kabupaten Tapanuli Tengah (2013-2033) serta mengetahui lokasi fungsi kawasan terbaik dalam kaitannya menghindari daerah rawan banjir di Kecamatan Sarudik. Penelitian dilakukan menggunakan metode analisis spasial berbasis sistem informasi geospasial. Teknik yang digunakan adalah teknik overlay dan scoring terhadap peta - peta yang dibutuhkan untuk mendapatkan informasi lokasi dan keterangan sesuai kebutuhan yang telah direncanakan sebelumnya. Fotometri dilakukan menggunakan drone yang hasil fotonya diolah untuk dapat dipergunakan sebagai peta dasar digitasi penggunaan lahan aktual di Kecamatan Sarudik. Penelitian mengenai sebaran dan kondisi daerah resapan Kecamatan Sarudik mendapatkan hasil bahwa terdapat kondisi daerah resapan tidak kritis 4.306,61 Ha (95,29%), dan kondisi daerah resapan kritis 212,75 Ha (4,71%). Tingkat kesesuaian fungsi kawasan di Kecamatan Sarudik dengan rencana pola ruang RTRW Kabupaten Tapanuli Tengah (2013-2033) adalah Hutan Lindung (99,90%), Hutan Produksi Konversi (91.34%), Kawasan Perikanan (99,99%), Pemukiman (99,99%), dan Sempadan Sungai (70,04%). Sebagai upaya menghindari dampak banjir, diperoleh lahan seluas 242,07 Ha yang berada diluar daerah kerawanan banjir dan memiliki kondisi daerah resapan yang baik
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".