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Beyond Survival: Developing Sustainable and Resilient Refugee Shelters through Biomimicry

2024· article· en· W4400317281 on OpenAlex

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.

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicArchitecture and Cultural Influences
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiomimeticsAdaptabilityHuman settlementEngineeringRefugeeResilience (materials science)Adaptation (eye)SustainabilityArchitectural engineeringManagement scienceComputer scienceConstruction engineeringEcologyArtificial intelligencePolitical science

Abstract

fetched live from OpenAlex

Abstract This study explores the use of biomimicry as a vital response to the urgent demand for sustainable and resilient refugee shelters. Recognizing the challenges faced by displaced populations due to climate disasters, it emphasizes the critical need for substantial improvements in shelter solutions. By embracing biomimicry principles that emulate nature’s efficiency, resilience, and adaptability, the study proposes environmentally conscious and resource-efficient shelter structures under a practical framework that meets immediate needs, contributes to climate mitigation, and harnesses nature’s innovative potential. The research methodology involves a comprehensive investigation utilizing architectural modelling, drawings, and the comparative analysis of various shelters. Through the meticulous assessments of photographs, structures, and diagrams, alongside extensive data aggregation, an informed analysis is presented. Utilizing computational tools and drawing inspiration from nature’s resilient strategies, this study presents a design methodology that applies the sustainable principles of biomimicry to effectively balance the construction of new settlements with the preservation of existing ecologies. By analysing diverse refugee shelter designs, including tarp-based tents, traditional shelters using local and industrial materials, transitional shelters, shelter kits, and climate-adapted tents, the study presents a practical biomimetic framework that responds to their limitations. Addressing challenges such as structural integrity, insulation, and adaptability, biological strategies are abstracted and applied through computational design processes to provide a tailored framework to assist designers in the development of refugee shelters. Amidst the exacerbating effects of climate change, this research highlights the untapped potential of biomimicry in creating sustainable and resilient refugee shelter settlements. By integrating advanced computational tools and a nuanced understanding of biological models, it establishes a framework for effective shelter development that prioritize sustainable and resilient shelter solutions worldwide.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.849
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 studies0.0010.003
Scholarly communication0.0010.001
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 head0.225
Teacher spread0.205 · 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