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Record W4413825266 · doi:10.1038/s42949-025-00259-z

Assessing the exposure of buildings to long-term sea level rise across the Global South

2025· article· en· W4413825266 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

Venuenpj Urban Sustainability · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsMcGill UniversityUniversity of Victoria
Fundersnot available
KeywordsTerm (time)Sea level riseSea levelEnvironmental scienceGeographyClimatologyPhysical geographyOceanographyGeologyClimate change

Abstract

fetched live from OpenAlex

Future sea levels are expected to rise, resulting in the progressive inundation of coastal cities. Because the spatio-temporal progression of this inundation is complex, few estimates have been made of how sea level rise will impact specific features of the built environment beyond 2100. Here we provide a first-order assessment of the exposure of buildings to sea level rise from satellite observation in Africa, Southeast Asia, and South and Central America. We define an inundation metric as a function of Local Sea Level Rise (LSLR) and consider this metric across a wide range of possible multi-century LSLR Values. Of the 840 million buildings in the study region, we find ~3.0 million at risk of inundation with 0.5 m LSLR, increasing to ~45 million with 5 m LSLR, and ~136 million with 20 m LSLR. Our results highlight geographic variability in exposure and demonstrate the benefits that low-emissions pathways imply for preserving built environments.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.027
GPT teacher head0.307
Teacher spread0.280 · 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