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Record W4392241737 · doi:10.18280/ijsdp.190217

Earthquake Resilience of Traditional Nias Island Houses: Lessons from the 2004 and 2005 Earthquakes

2024· article· en· W4392241737 on OpenAlexvenueno aff
Abdul Hakam, Mhd. Nur, Purwo Husodo, Novalinda Novalinda, Syafrizal Syafrizal, Zulqaiyyim

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPacific and Southeast Asian Studies
Canadian institutionsnot available
FundersUniversitas Andalas
KeywordsResilience (materials science)SeismologyUrban seismic riskEarthquake scenarioGeographyGeologyForensic engineeringEnvironmental planningEnvironmental resource managementSeismic hazardEnvironmental scienceEngineering

Abstract

fetched live from OpenAlex

For the past two centuries, Nias, an island located in the Indian Ocean on the west coast of Sumatra, has been regularly rocked by earthquakes.It was recorded that devastating earthquakes occurred in 1843, 1861, and finally in 2004 and 2005.Facing this situation, the people of Nias Island have taken the initiative to respond to earthquake disasters in the form of cultural heritage to avoid and save themselves from various possible risks of earthquakes by building traditional earthquake resilience houses.This article aims to explain the existence of these traditional earthquake-resilience houses in the face of earthquake natural disasters.The method used is a combination of historical and architectural by utilizing various kinds of past data from earthquake resilience houses.The results of the research show that this earthquake-resilience house has been built for centuries and has a unique architectural model and resilience so it does not collapse easily even when shaken by a strong earthquake.Traditional houses are made of strong wood and built based on the local wisdom of the local community.Throughout its history, this house has been able to save many people living in these houses from earthquakes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

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

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 head0.034
GPT teacher head0.299
Teacher spread0.265 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2024
Admission routes1
Has abstractyes

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