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Record W4417266986 · doi:10.1051/e3sconf/202567706002

Cost analysis of repairing damage to coastal protection structures in Pariaman City, West Sumatra

2025· article· fr· W4417266986 on OpenAlexaff
Monika Natalia, Rahmi Hidayati, Anik Rapis, Masrillayanti, Zulfira Mirani, Suhendrik Hanwar, Jumyetti Jumyetti, Sulleyman Sourkan

Bibliographic record

VenueE3S Web of Conferences · 2025
Typearticle
Languagefr
FieldEngineering
TopicEarthquake and Tsunami Effects
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsRevetmentRubbleSafeguardPrioritizationShoreGovernment (linguistics)Cost–benefit analysisCost analysis

Abstract

fetched live from OpenAlex

Pariaman City is in West Sumatra Province, with a land area of 73.36 km² and a coastline length of 12.7 km. The local government has constructed coastal protection structures such as groins, jetties, and revetments to safeguard the shoreline and preserve the coastal ecosystem from damage caused by waves, abrasion, accretion, and erosion. Over time, several components of the groin, jetty, and revetment structures have experienced deterioration, necessitating repair. This study aims to identify the damage to each structure, propose appropriate repair solutions, and estimate the repair cost budget.The assessment of damage, repair guidelines, and prioritization of interventions are carried out based on the Regulation of the Minister of Public Works No. 09/PRT/M/2010 and the Circular Letter of the Minister of Public Works No. 01/SE/M/2011. This study found that the primary cause of damage was the disintegration of the rubble arrangements in several sections. The recommended repair solution involves adding new rocks and rearranging existing ones to interlock securely. The estimated cost of repairs is IDR 662,014,140.00.

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)0.001
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score0.189

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.277
Teacher spread0.252 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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