Evaluasi Kinerja dan Dampak Jeti Muara Sungai Bogowonto terhadap Perubahan Garis Pantai
Bibliographic record
Abstract
Abstract River mouth closure could pose significant challenges, when important infrastrucutre is operated nearby. For instance, the Yogyakarta Internasional Airport, which is located in the vicinity of Bogowonto River Mouth. A 300 m length of training jetty was built to mitigate the closure. After several years, the performace of its effectiveness is important to be evaluated. This research was done by satellite imagery analysis using CoastSat, wave characteristic analysis based on ERA5 data, numerical simulation using DELFT3D, and field observations. ERA5 indicates that wave heights might reach 3.5 – 4 meters during storm seasons, with an average period of 9 to 11 seconds, with dominant direction is from the south. Local current from east to west influences local sediment transport with a predominantly westward trend. This trend was seen in DELFT3D simulation as well. Satellite imagery reveals sediment accumulation on both river mouth sides. Although erosion is typically expected on the downdrift side, no evidence of such erosion was observed during the study period. The rate of sediment accretion was approximately 12 m/year on the eastern side and 9.5 m/year on the western side of the jetty. It was concluded that the jetty has functioned effectively in preventing river mouth closure, and to date, no significant erosional impact was detected. Keywords : Flood mitigation, jetty, river mouth closure, sediment transport, YIA
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".