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Record W2314473776 · doi:10.1061/9780784478745.105

Linking Disaster Resilience and Sustainability

2014· article· en· W2314473776 on OpenAlexaff
Tonatiuh Rodríguez-Nikl, Matthew V. Comber, Simon Foo, Sally J. Gimbert Carter, Lionel Lemay, Panagiotis Koklanos, Lindsey Maclise, Martha G. VanGeem, Mark D. Webster

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsPublic Works and Government Services Canada
Fundersnot available
KeywordsSustainabilityResilience (materials science)SalientEngineeringNatural disasterEnvironmental resource managementEngineering ethicsRisk analysis (engineering)Environmental planningBusinessComputer scienceEnvironmental scienceGeographyEcology

Abstract

fetched live from OpenAlex

Structural engineers often limit their involvement in sustainability to material selection and recycling. Few structural engineers recognize the relationship between sustainability and disaster resilience. In response, the Sustainability Committee of the Structural Engineering Institute wrote a committee report to raise awareness of and provide guidance on the pertinent issues. This paper highlights the salient parts of the committee report. The introduction explains the relationship between sustainability and resilience and reviews the impacts of natural disasters. The following sections discuss general consideration for resilient design and summarize efforts to promote resilience and guidance for resilient design. Next are discussed current efforts to quantify the connection between disaster resilience and sustainability. The paper concludes with suggestions for structural engineers who are interested in supporting disaster resilience and sustainability.

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)0.000
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 score0.481
Threshold uncertainty score0.279

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 head0.003
GPT teacher head0.206
Teacher spread0.203 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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