Assessing the exposure of buildings to long-term sea level rise across the Global South
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.
Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it