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Record W2603933567 · doi:10.2495/sdp-v12-n7-1203-1214

Shoreline changes analysis in Kuwaru coastal area, Yogyakarta, Indonesia: An application of the Digital Shoreline Analysis System (DSAS)

2017· article· en· W2603933567 on OpenAlexvenueno aff
Bachtiar W. Mutaqin

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsShoreGeologyOceanographyEnvironmental scienceGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.233
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations63
Published2017
Admission routes1
Has abstractyes

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