Coastline Change Detection by Using DSAS Analysis Method along Kelantan Coast, Peninsular of Malaysia
Bibliographic record
Abstract
Kelantan coast is an intimidating and dynamic coastal area in Peninsular Malaysia. Thus, this study focuses on determining the change in the coastal areas between 1955, 1974, 1991, 2010, and 2019. Topographic map, SPOT-5 J, and Landsat-5 thematic mapper were used to arbitrate temporal changes of coastal areas. DSAS analysis method, linear regression rate (LLR), and end point rate (EPR) were utilized to measure the changes in the coastal line. As a result, the maximum LLR rate appeared to be −20.51 m/year and +25.68 m/year for the minimum, perceived as the highest erosion and accretion rate, respectively. Variability of the ERP rate within the year shows the dynamic of the Kelantan coast that is strongly influenced by the Northeast Monsoon (October – February) season. The greatest erosion rate occurred between 1974 - 1991, around −126.07 m/year, while maximum accretion was +111.33 m/year in 2010 - 2019. Human interference, such as built rock revetment and breakwaters on the river mouth, contributed to the high rate of coastal changes. The output of the study will provide helpful information for the coastal area development plans for the local government subsequently.
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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".