Projected Curve Number (CN) Changes and Surface Runoff Response to Corrected Land Cover Dynamics in the Citarum-Majalaya Catchment
Bibliographic record
Abstract
Land use and land cover changes that occur rapidly and without proper control have the potential to increase runoff discharge and elevate the risk of flooding.This study is crucial for analyzing land cover changes that influence runoff discharge, evaluating potential future scenarios, and providing a scientific foundation for the formulation of policy recommendations.The assessment of land cover changes on surface runoff is carried out by observing variations in Curve Number (CN) values.The analysis and projection of CN values are based on corrected land cover map data and trend models.Two projection approaches are used: the first assumes no mitigation efforts, while the second is based on spatial planning strategies.The first approach yields a higher CN value and shows an increasing trend, with an estimated CN value of 81.52 in 2044, rising to 82.17 by 2050.The second approach yields a Curve Number (CN) value of 79.87 in 2044, which increases slightly to 79.93 by 2050 (still below 80.00).The difference in maximum runoff between the two approaches continues to grow, from 0.11 m³ /s in 2025 to 0.51 m³ /s in 2044.Land cover changes based on spatial planning scenarios demonstrate a significant reduction in CN values and surface runoff.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".