Analisis Perubahan Dinamika Abrasi dan Akresi Garis Pantai di Kota Kupang Berbasis Teknologi Penginderaan Jauh
Bibliographic record
Abstract
Shorelines are dynamic and perpetually evolving due to hydro-oceanographic variables and anthropogenic activity, which influence the processes of erosion and deposition. This study seeks to quantify shoreline alterations and examine the extent of erosion and deposition in coastal Kupang City by employing remote sensing technologies on Landsat image datasets from 2014, 2018, and 2023, obtained from USGS, in conjunction with Geographic Information System (GIS). Analytical methods were implemented via the Digital Shoreline Analysis System (DSAS) within GIS. The results indicated that alterations manifested as abrasion and accretion with differing magnitudes. From 2014 to 2018 and from 2018 to 2023, notable alterations transpired, with the maximum erosion value attaining -37.98 m in the Kelapa Lima sub-district and the peak deposition measuring 187.09 m in the Kota Lama sub-district. From 2014 to 2018, the regions impacted by abrasion in Kelapa Lima, Kota Lama, and Alak measured 4.01 hectares, 0.63 hectares, and 1.24 hectares, respectively. This technology enables the management and analysis of visual data, offering great temporal resolution, cost-effectiveness, and extensive coverage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".