PENGARUH PERUBAHAN TATA GUNA LAHAN TERHADAP NILAI CURVE NUMBER PADA DAS SAROKAH
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%.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.008 |
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".