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Record W2966118552 · doi:10.1007/s10901-019-09697-5

A building classification scheme of housing stock in Malawi for earthquake risk assessment

2019· article· en· W2966118552 on OpenAlexaff
Panos Kloukinas, Viviana Novelli, Innocent Kafodya, Ignasio Ngoma, John H G Macdonald, Katsuichiro Goda

Bibliographic record

VenueJournal of Housing and the Built Environment · 2019
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsWestern University
FundersEngineering and Physical Sciences Research Council
KeywordsStock (firearms)UrbanizationGeographyVulnerability (computing)Seismic riskEnvironmental resource managementCivil engineeringBusinessComputer scienceEnvironmental scienceEngineeringEconomic growthComputer securityEconomics

Abstract

fetched live from OpenAlex

This study presents a building classification scheme for residential houses in Malawi by focusing upon informal construction, which accounts for more than 90% of housing in the country with the highest urbanisation rate in the world. The proposed classification is compatible with the Prompt Assessment of Global Earthquakes for Response (PAGER) method and can be used for seismic vulnerability assessments of building stock in Malawi. To obtain realistic proportions of the building classes that are prevalent in Malawi, a building survey was conducted in Central and Southern Malawi between 10th and 20th July 2017. The results from the survey are used to modify the PAGER-based proportions of main housing typologies by reflecting actual housing construction in the surveyed areas. The results clearly highlight the importance of using realistic building stock data for seismic risk assessment in Malawi; relying on global building stock information can result in significant bias of earthquake impact assessment.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.269

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 head0.013
GPT teacher head0.242
Teacher spread0.229 · 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 designSimulation or modeling
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

Citations18
Published2019
Admission routes1
Has abstractyes

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