Shoreline changes analysis in Kuwaru coastal area, Yogyakarta, Indonesia: An application of the Digital Shoreline Analysis System (DSAS)
Bibliographic record
Abstract
In the last 20 years, Kuwaru coastal area has been under constant threat from both physical and nonphysical processes. Such threat is exacerbated by the fact that this coastal area is mainly composed of loose sediment materials that are easily eroded and re-deposited as a response to disturbance. One of the threats is shoreline change. This research aims to analyze the shoreline change in Kuwaru coastal area with the aid of the Digital Shoreline Analysis System. Shoreline changes in the coastal area affects tourism and fishery activities, causes loss of land, and damages infrastructures, all of which mark the urgency of shoreline change analysis. Shoreline change is identified with an interdisciplinary approach, i.e. the integration of remote sensing technology and Geographic Information Systems (GIS). The topographic maps published in 1995 and the multi-temporal satellite imagery in 2006-2015 are used as initial information in acquiring necessary shoreline data. Shoreline change is analyzed using the End Point Rate (EPR) technique. Shoreline data in 1995 is used as the baseline in analyzing the rate of shoreline change. Furthermore, transects spaced at 50-meter intervals along the shoreline stretch landward and perpendicularly to the baseline. EPR results in either positive or negative values that indicate accretion or erosion, respectively. This research finds that the shoreline of Kuwaru coastal area has changed significantly since 1995. In general, from 1995 to 2015, the shoreline shifted by more than 50 meters landward. Extreme weather during the East Monsoon is one of the many factors that induce destructive waves in the research area. Sea waves of up to 5 meters in height hit the southern part of the research area from the southeast. Consequently, coastal mitigation efforts, which factor in the dynamics of coastal processes, have to be implemented immediately through structural mitigation or non-structural mitigation.
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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 | 0.002 | 0.002 |
| 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) | 0.001 | 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".