Inhibition of Ekortikus Island Expansion in Banyuasin Estuary, South Sumatra Modelling by Using Finite Volume Method with Unstructured Mesh
Bibliographic record
Abstract
Ekortikus Island was formed through continuous sedimentation in Banyuasin Estuary (BAE) in Banyuasin District, South Sumatra. Based on satellite data, Ekortikus Islands area is expanding dramatically, 22 times greater in 21-year period. This expansion narrowed the estuary and disturbing estuary function, especially related to water run off from water cathment area. Ekortikus expansion was caused by high sedimentation and flow distribution mode which were formed by the fusion of two river flows assembled in the estuary, confounded by tidal flow dynamic with amplitude of 0.6 – 3.9 m. An experiment was conducted to simulate development of Ekortikus Island with or without placement of a potential barrier. The simulation used Finite Volume Method (FVM) with triangle unstructured mesh. Simulation with normal conditions or without treatment, in which the flow is Banyuasin greater than the flow Lalan turn out Ekortikus island increases toward the Southwest and due to tidal flow, Ekortikus Island increases towards the Northwest. Simulation could also manipulate the future form of the Island when a potential barrier was placed to direct flows of both rivers. Treatment simulation, by changing the flow Banyuasin becoming greater than Lalan flow, altered the location of the deposition of sediment to another place and Ekortikus Island area expansion was reduced.
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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 | 0.000 | 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) | 0.002 | 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".