Analisa Laju Erosi dan Arahan Penggunaan Lahan Berbasis Sistem Informasi Geografis (SIG) pada DAS Mayang Hulu Kabupaten Jember Jawa Timur
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.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".