Application of Geographic Information Systems and Sediment Routing Methods in Sediment Mapping in Krueng Jreu Sub-Watershed, Aceh Province, Indonesia
Bibliographic record
Abstract
Land management in the Krueng Jreu sub-watershed (Aceh Province, Indonesia) that did not follow soil and water conservation methods encouraged erosion. This can lead to silting of rivers or irrigation canals due to sediment deposition. Limited tools were the main reason for the infrequent measurement and mapping of these sediments in watersheds. Therefore, this study aims to conduct sedimentary mapping using GIS techniques combined with the sediment routing method to successfully produce a map of sediment assessment criteria for the Krueng Jreu sub-watershed area from 2010 to 2019. Rainfall and spatial data from the Krueng Jreu sub-watershed were analyzed to obtain several parameters of surface runoff, peak discharge, erodibility, slope, the value of ground cover, and land management. The results show that the Krueng Jreu sub-watershed was included in the wet climate type. The type of land use classification of savanna accounted for the most significant runoff, and land use type of open soil gave the smallest runoff. The maximum erosion found in the secondary dryland forest type land classification. It was known that the type of secondary dryland forest land use was the most significant contributor to sediment occurrence in the Krueng Jreu sub-watershed area.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".