Earthquake Resilience of Traditional Nias Island Houses: Lessons from the 2004 and 2005 Earthquakes
Bibliographic record
Abstract
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".