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Record W2581700003 · doi:10.1193/072516eqs120m

Factors Influencing Post‐Earthquake Decisions on Buildings in Christchurch, New Zealand

2017· article· en· W2581700003 on OpenAlex
Jenna Jihyun Kim, Kenneth J. Elwood, Frédéric Marquis, Stephanie E. Chang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEarthquake Spectra · 2017
Typearticle
Languageen
FieldEngineering
TopicSeismic Performance and Analysis
Canadian institutionsJacobs (Canada)University of British ColumbiaGolder Associates (Canada)
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDemolitionOccupancyLogistic regressionLegislationForensic engineeringGeographyEngineeringCivil engineeringPolitical scienceComputer science

Abstract

fetched live from OpenAlex

The high demolition rate (∼60%) of reinforced concrete (RC) buildings that generally performed well in Christchurch, New Zealand, has been one of the most important lessons from the Canterbury Earthquakes. In an effort to understand such an outcome, various factors influencing the post‐earthquake decisions on buildings (demolition or repair) are explored, focusing on multi‐story RC buildings in Christchurch Central Business District (CBD). Using empirical data, logistic regression analysis was conducted to explain the likelihood of building demolition. Several explanatory factors were found to be statistically significant: assessed damage, occupancy type, heritage status, number of floors, and construction year. From in‐person interviews conducted in New Zealand, contextual factors such as insurance policy and changes in legislation were also found to play a significant role in the post‐earthquake decisions on buildings.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.244
Teacher spread0.225 · 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