PENGARUH PERUBAHAN TATA GUNA LAHAN TERHADAP NILAI CURVE NUMBER PADA DAS SAROKAH
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
Perubahan tata guna lahan pada suatu DAS akan mempengaruhi karakteristik hidrologi pada DAS tersebut. Selain curah hujan yang ekstrim, perubahan tata guna lahan merupakan salah satu faktor penyebab terjadinya banjir. Penelitian ini dilakukan untuk mengetahui perubahan nilai CN akibat perubahan tata guna lahan pada DAS Sarokah. Analisis tata guna lahan dilakukan dengan melakukan training objek pada data citra satelit Landsat 7, Landsat 8 dan Sentinel 2A. Tata guna lahan DAS Sarokah dalam periode tahun 2002-2013 terdapat pengurangan luasan sebesar 9,03% untuk area sawah dan peningkatan luasan perkebunan sebesar 5,83%. Pada periode 2013 - 2023 terdapat peningkatan luasan lahan terbangun sebesar 3.16% dan penurunan luasan sebesar 5,26% untuk area persawahan. Perubahan tata guna lahan 2023-2042 berdasarkan RTRW Kabupaten, akan terjadi peningkatan luasan lahan terbangun (Built Up) sebesar 29.15% dan 19.69% untuk area persawahan. Namun untuk area hutan/pepohonan dan area perkebunan mengalami pengurangan lahan yaitu 17.36 % dan 23.96%. Berdasarkan perubahan tata guna lahan 2023-2043 kenaikan nilai CN Tahun 2043 pada Sub DAS S15, S6 dan S14 adalah yang tertinggi yaitu 16.5%, 13.2% dan 10.8%.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.010 |
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