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Record W2066275084 · doi:10.1193/1.2201668

Housing Reconstruction in Northern Sumatra after the December 2004 Great Sumatra Earthquake and Tsunami

2006· article· en· W2066275084 on OpenAlexaff
Christopher S. Meisl, Sahar Safaie, Kenneth J. Elwood, Rishi Gupta, Reza Kowsari

Bibliographic record

VenueEarthquake Spectra · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsUniversity of British Columbia
FundersNational Institute on Aging
KeywordsGovernment (linguistics)GeographyQuality (philosophy)GeologySeismology

Abstract

fetched live from OpenAlex

The 26 December 2004 earthquake and tsunami resulted in over 100,000 damaged or destroyed homes and over 500,000 internally displaced people in northern Sumatra. Reconstruction and recovery from these massive losses requires the coordination of many stakeholders, including multiple levels of government, nongovernment relief organizations, donors, and the people of northern Sumatra. Although efforts have been taken by the Government of Indonesia to develop standards for the reconstruction of houses and establish a coordinating body, the reconstruction effort in Sumatra still faces many challenges. A broad range of housing types, with varying degrees of construction quality, have been constructed as part of the recovery effort. A field study team visited Banda Aceh, Meulaboh, and Nias seven months after the December event and documented the process of reconstruction, the interaction of the stakeholders, and the types of housing construction.

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)0.001
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 score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.192
Teacher spread0.183 · 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

Citations19
Published2006
Admission routes1
Has abstractyes

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