Discharging Sediment Downstream: The Opportunities and Challenges of Sediment Management in Sutami Reservoir, Indonesia
Bibliographic record
Abstract
There is no flushing outlet facility and limited disposal sites for dredged material sediment in Sutami Reservoir.One preferable alternative to evacuate the sediment is directly downstream discharging dredged material.New research into such dredging techniques is therefore essential.This research focused on the technical opportunities and challenges of downstream discharging sediment from Sutami Reservoir.In addition, the HEC-RAS model was used to look at the impacts on the river downstream of the dam.Results showed that 400,000 m 3 should be dredged at two sites (Dempok and Sumberpetung), extended the pipeline to release the slurry material downstream, and two booster pumps installed to maximize dredging productivity.There were no significant impacts on the riverbed alteration downstream of the dam.The slurry material flowed and deposited in Wlingi Dam and could be flushed periodically to avoid excess deposits.There were opportunities to save the cost of disposal area and support the replenishment of sediment.Meanwhile, the challenges were the need to monitor the environmental issues related to water quality downstream, cost investment, and water loss, which cannot re-enter the storage as conventional dredging.Finally, discharging sediment downstream was technically feasible and the best solution for sediment management in Sutami Reservoir.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".