Analisa Laju Erosi dan Arahan Penggunaan Lahan Berbasis Sistem Informasi Geografis (SIG) pada DAS Mayang Hulu Kabupaten Jember Jawa Timur
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Permasalahan yang terjadi di DAS Mayang terutama di wilayah hulu, disebabkan oleh pemanfaatan Sungai Mayang yang tidak tepat oleh masyarakat. Perubahan tata guna lahan di wilayah hulu DAS menyebabkan air hujan yang turun mengalir langsung ke sungai karena kurangnya tumbuhan yang dapat menahannya, sehingga menyebabkan terjadinya erosi dan sedimentasi di Sungai Mayang serta perlu adanya upaya untuk manajemen DAS. Studi ini menggunakan bantuan software ArcGIS yang dikolaborasikan dengan model ArcSWAT untuk perhitungan nilai erosi dan sedimentasi. Hasil simulasi pada kondisi eksisting diperoleh nilai potensi laju erosi 43,356 ton/ha/tahun dan potensi sedimentasi 27,778 ton/ha/tahun. Hasil analisis indeks bahaya erosi diperoleh dua kriteria yaitu sedang dengan luas 28.750 ha atau 64,463 % dari luasan total dan tinggi dengan luas 15.849 ha atau 35,537 % dari luasan total. Berdasarkan hasil simulasi setalah dilakukan arahan penggunaan lahan, nilai potensi laju erosi diperoleh 28,604 ton/ha/tahun dan sedimentasi 17,969 ton/ha/tahun serta nilai indeks bahaya erosi yang lebih rendah dari kondisi eksisting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it